The DOI matches its Crossref record; 97 of the 98 references it cites were found in a registry or an index; 7 of the 107 sources its claims cite were checked for support.
Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions
Open accessAbstract
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review examines Edge AI—AI, machine learning, deep learning, and federated learning deployed at the network edge—for early detection of chronic and infectious diseases, arguing it can enable real-time, privacy-preserving health prediction. It surveys applications, models, and challenges, concluding that privacy, data quality, bias, interoperability, and integration with existing health systems must be addressed for adoption.
Key findings
- Edge AI can process wearable, medical-device, and EHR data locally for real-time early prediction, while federated learning enables collaborative model training without centralizing patient data. One cited example is the EXAM federated learning model, which predicted COVID-19 oxygen requirements using data from 20 institutions and achieved a mean AUC of 0.92 for outcomes within 24 and 72 h.
- Reported model performances in reviewed studies include random forest achieving 82.26% accuracy for diabetes prediction (Naive Bayes worst at 70.56%); a decision-tree classifier achieving 98% accuracy for heart disease prediction and 99% for classifying subject position; Alzheimer’s prediction reaching 83% test accuracy; and NYUTron achieving an AUC of 78.7–94.9%, improving AUC by 5.36–14.7% over traditional models.
- Federated learning studies cited include a hybrid classifier-based FL framework improving cardiovascular disease prediction accuracy by 1.5% with 1.6% fewer classification errors; heart disease FL reaching around 89% accuracy on the UCI benchmark dataset; dynamic federated meta-learning improving rare disease prediction accuracy by 13.28%; and COVID-19 mortality FL models outperforming local models (LASSO federated model at three hospitals; MLP federated model at all five hospitals).
- The paper identifies privacy/security, data quality/accuracy, model bias, interoperability, and integration with existing health systems as key challenges, with future directions including privacy-preserving algorithms, standardization, bias mitigation, and APIs/middleware.
Methods
- Systematic literature review using keywords and search criteria C1–C4, searching Scopus, Google Scholar, and PubMed, restricted to articles published between 2019 and 2023, and organized around research questions RQ-1 to RQ-4.
- The initial search found 556 references from Scopus, 565 from Google Scholar, and 198 from PubMed (total 1319); after eliminating duplicates and incomplete references, 280 remained; further title, abstract, and full-text screening eliminated 182, leaving 98 eligible references (excluding background citations).
- Analysis is a narrative synthesis with summary tables of Edge AI and federated learning applications, methods, results, and limitations.
Limitations
- Stated: Edge AI for early health prediction faces privacy and security risks, data quality and accuracy problems, model bias, interoperability issues, and challenges integrating with existing health systems.
- Stated: The review’s methodology did not aim for complete coverage, and the search was restricted to 2019–2023 and three databases. Included federated learning studies often lack details on dataset size/diversity, generalizability beyond specific datasets (e.g., MIMIC-III), real-world examples, and a unified classification of FL challenges.
- Evident from the text: This is a review, not a primary validation study; reported performance numbers come from cited studies and are not independently reproduced, and no meta-analysis or formal risk-of-bias assessment is described.
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Verification
Claims the paper makes
82 Not checked yet · 31 Own interpretation · 27 No basis given · 3 Partly supported · 3 Own result · 2 Supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI algorithms can efficiently analyze data and uncover hidden patterns and correlations that indicate potential diseases [24].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.3. Prediction of Future Health Outcomes
What the sources say
- Partly supportedcites [24] Mohsen, 2022, Artificial intelligence-based methods for fusion of electronic health records and imaging data, Sci. Rep. sourceExperimental — accuracy not yet measured
The Results (Clinical outcomes and machine learning models; page not provided) describe ML models that combine patient data to diagnose and predict disease, including a model that learns correlations among data modalities. The paper supports the disease-detection point, but the cited passages do not establish that the algorithms analyze data efficiently or specifically uncover hidden patterns. (LLM, conf=0.83)
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- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This variability can impact the performance and generalizability of AI models, making it essential to address data variability challenges.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 7.2. Data Quality and Accuracy
What the sources say
- Partly supportedcites [97] Souza, J., Caballero, I., Vasco Santos, J., Lobo, M., Pinto, A., Viana, J., Sáez, C., Lopes, F., and Freitas, A. (2022). Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications. J. Biomed. Inform., 136. sourceExperimental — accuracy not yet measured
The Introduction says temporal variability in healthcare data poses challenges for machine learning and that variability across sources or time can lead to inaccurate conclusions. It does not specifically address data collected at the edge or establish effects on AI-model performance and generalizability. (LLM, conf=0.82)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The MLP federated model performed better than the MLP local model at all five hospitals.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Supportedcites [91] Vaid, 2021, Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach, JMIR Med. Inform. source
The Discussion explicitly states that federated MLP outperformed local MLP at all hospitals in this study of five hospitals. The Abstract says the federated models outperformed their local counterparts at four hospitals, so the paper is internally inconsistent on this point. (LLM, conf=0.85)
- Supportedcites [92] Vaid, A., Jaladanki, S.K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., De Freitas, J.K., and Wanyan, T. (2020). Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19. medRxiv, Preprint. source
The Discussion (page unavailable) explicitly states that federated MLP outperformed local MLP at all hospitals. The paper identifies these as five hospitals and describes the outcome as seven-day mortality in hospitalized COVID-19 patients. (LLM, conf=0.86)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
None of the sources cited for this claim have been checked yet.
In the paper
“AI algorithms can be used to analyze these data to identify patterns and trends that may indicate disease onset [1,2].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
What the sources say
plainconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“In healthcare, edge computing is particularly useful in applications that require real-time monitoring and decision making, such as wearable devices for patient monitoring and remote diagnosis and treatment [7,8].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
What the sources say
- Not checked yetcites [7] Ray, 2019, Edge computing for Internet of Things: A survey, e-healthcare case study and future direction, J. Netw. Comput. Appl. source
- Not checked yetcites [8] Sodhro, 2019, Mobile edge computing based QoS optimization in medical healthcare applications, Int. J. Inf. Manag. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“This information can then be used to develop personalized interventions, treatments, and preventive measures to improve an individual's health and prevent the onset of chronic diseases [13].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
What the sources say
- Not checked yetcites [13] Hong, 2021, Wearable sensors for continuous oral cavity and dietary monitoring toward personalized healthcare and digital medicine, Analyst source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“One of the most important benefits of early health prediction is the early detection of chronic diseases such as diabetes, cardiovascular disease, and cancer [14][15][16].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
What the sources say
- Not checked yetcites [14][15][16]
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In the paper
“Analysis of medical data can fine-tune drug dosages for individual patients based on their characteristics and past responses [33].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
What the sources say
- Not checked yetcites [33] Coronato, 2020, Reinforcement learning for intelligent healthcare applications: A survey, Artif. Intell. Med. source
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, this approach is a valuable privacy and security enhancement because it allows sensitive information to be processed locally.By reducing reliance on the transmission of such data to a central location, the risks associated with long-distance data transmission are mitigated, and the protection and confidentiality of critical information are strengthened.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“In addition, the keen eye of deep learning algorithms sifts through electronic health records, unearthing valuable insights that pave the way for predicting patient outcomes [31,32].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
What the sources say
- Not checked yetcites [31] Fraiwan, M., Audat, Z., Fraiwan, L., and Manasreh, T. (2022). Using deep transfer learning to detect scoliosis and spondylolisthesis from X-ray images. PLoS ONE, 17. source
- Not checked yetcites [32] Panayides, 2020, AI in Medical Imaging Informatics: Current Challenges and Future Directions, IEEE J. Biomed. Health Inform. source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 59
- It may state two separate claims as one. 48
- The quote only partly states this claim. 3
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI in healthcare typically uses electronic health records to identify patients at risk for chronic diseases such as heart disease and diabetes [24,25].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
What the sources say
- Partly supportedcites [24] Mohsen, 2022, Artificial intelligence-based methods for fusion of electronic health records and imaging data, Sci. Rep. sourceExperimental — accuracy not yet measured
The Results section on early prediction reports AI studies that combined EHRs with medical imaging to predict whether a person might develop a disease. But the paper does not establish that this is a typical use of AI in healthcare, and its reported cardiovascular applications concern diagnosis rather than future risk. Its disease list includes diabetic retinopathy, not prediction of diabetes itself. (LLM, conf=0.86)
- Partly supportedcites [25] Briganti, 2020, Artificial Intelligence in Medicine: Today and Tomorrow, Front. Med. sourceExperimental — accuracy not yet measured
Section 2.1.2 (Cardiovascular Risk; page not supplied) says AI has been applied to electronic patient records to predict cardiovascular disease risk. Section 2.3 (Endocrinology; page not supplied) discusses glucose monitoring for people who already have diabetes, not using electronic health records to identify people at risk of developing it. The source also does not establish that this is how AI in healthcare is typically used. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The authors in [56] discuss the development of a federated learning model called EXAM, which predicts the oxygen requirements of COVID-19 patients using data from 20 institutions worldwide.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.2. Early Detection of Chronic and Infectious Diseases
What the sources say
- Supportedcites [56] Dayan, 2021, Federated learning for predicting clinical outcomes in patients with COVID-19, Nat. Med. source
The Methods (EXAM score) state that EXAM was trained using federated learning to predict patients’ oxygen-support requirements. The Results (The EXAM Model Architecture) describe its oxygen-therapy outcome labels, and the Discussion refers to data from 20 sites. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
In the paper
“Experimental results of the proposed framework show an improved performance, an accuracy up to 98.5%, and data security.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1.
What the sources say
- Not checked yetcites [34] Singh, 2022, Edge computing based secure health monitoring framework for electronic healthcare system, Clust. Comput. source
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further screening of the title, abstract, and full text allowed the elimination of 182 references that addressed issues far from the main topic of this review article.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 4.2. Source Selection and Approach
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Scopus, Google Scholar, and PubMed provide comprehensive coverage of scholarly works on the subject.
- Rests on an assumption: The search criteria C1-C4 and the 2019-2023 date restriction are adequate to identify relevant literature.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The final number of references eligible for this study was 98.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 4.2. Source Selection and Approach
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The final 98 references are representative of the literature on Edge AI for early health prediction.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the total number of references, 1319, was reduced to 280 after eliminating duplicates and incomplete references.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 4.2. Source Selection and Approach
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Scopus, Google Scholar, and PubMed provide comprehensive coverage of scholarly works on the subject.
- Rests on an assumption: The search criteria C1-C4 and the 2019-2023 date restriction are adequate to identify relevant literature.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The initial search for the above search criteria (C1-C4) found 556 references from Scopus, 565 references from Google Scholar, and 198 references from PubMed.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 4.2. Source Selection and Approach
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Scopus, Google Scholar, and PubMed provide comprehensive coverage of scholarly works on the subject.
- Rests on an assumption: The search criteria C1-C4 and the 2019-2023 date restriction are adequate to identify relevant literature.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated learning is substantially better than the local approach.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6.2. Federated Learning for Disease and Mortality Prediction
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In the paper
“Federated learning performs equally well as the centralized approach.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6.2. Federated Learning for Disease and Mortality Prediction
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Abstract
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“By combining the strengths of AI and edge computing, healthcare providers can create innovative solutions that improve public health outcomes, lower healthcare costs, and improve the overall patient experience [9,10].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
What the sources say
- Not checked yetcites [9] Kamruzzaman, 2022, New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities, J. Healthc. Eng. source
- Not checked yetcites [10] Rathi, 2021, An edge AI-enabled IoT healthcare monitoring system for smart cities, Comput. Electr. Eng. source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Edge computing can significantly reduce latency and increase processing speeds by bringing computing power close to the data source.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“By combining these data with other relevant information, such as demographic and environmental factors, AI can be used to make accurate predictions about future health outcomes and develop targeted interventions to prevent disease outbreaks [3,4].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
What the sources say
- Not checked yetcites [3] Bohr, A., and Memarzadeh, K. (2020). Artificial Intelligence in Healthcare, Academic Press. source
- Not checked yetcites [4] Rong, 2020, Artificial Intelligence in Healthcare: Review and Prediction Case Studies, Proc. Est. Acad. Sci. Eng.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Using edge computing, these devices can analyze vital signs in real time and alert healthcare providers to anomalies [40,41].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
What the sources say
- Not checked yetcites [40] Mohan, 2021, Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano, Adv. Hum.-Comput. Interact. source
- Not checked yetcites [41] Islam, 2022, Internet of Things- (IoT-) Based Real-Time Vital Physiological Parameter Monitoring System for Remote Asthma Patients, Proc. Int. Wirel. Commun. Mob. Comput. Conf.
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Wearable technology, which includes devices such as fitness trackers and smartwatches, is evolving into a comprehensive data repository that contains various details about a person's physical movements, heart behavior, and sleep dynamics during nighttime hours (see Figure 2).”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Growing privacy concerns have led to the emergence of federated learning, an innovative method of machine learning that enables the training of models with distributed data across numerous devices without the need to aggregate the data in a central repository.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This proactive approach enables healthcare providers to intervene quickly, prevent disease progression, improve health outcomes, and ultimately reduce overall healthcare spending.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is worth emphasizing that early health prediction promises to reduce healthcare costs by circumventing the need for later exhaustive and financially burdensome treatments.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Healthcare could be significantly transformed by edge computing, which enables the timely examination of significant amounts of data derived from wearable devices, medical instruments, and other related sources in real time.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By leveraging federated learning, physicians can harness the power of decentralized data while protecting individual privacy, ultimately advancing the field of predictive analytics in healthcare.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Moreover, the use of AI algorithms is proving to be valuable as it enhances the capabilities of healthcare providers in making informed and accurate diagnoses [29].These algorithms help formulate personalized medical plans that seamlessly integrate important aspects such as the patient's genetic makeup, medical background, and lifestyle habits.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
What the sources say
- Not checked yetcites [29] Topol, 2019, High-performance medicine: The convergence of human and artificial intelligence, Nat. Med. source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It has been used to analyze medical images such as X-rays and MRI scans, revealing complex patterns that significantly affect diagnostic accuracy.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The convergence of deep learning and reinforcement learning holds the potential to improve the analysis of medical data, ultimately leading to better treatment outcomes.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This technique holds great promise, especially in healthcare, where it has significant potential for predicting and forecasting disease.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“An excellent example of this application is the effective use of wearable devices equipped with sophisticated sensors that enable the seamless collection of vital signs, physical activity measurements, and other key health indicators.These data points can then be seamlessly processed and analyzed, facilitating the timely detection of early signs of disease.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Deep learning and reinforcement learning are two powerful AI techniques for medical data analysis [17,30].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
What the sources say
- Not checked yetcites [17] Xu, L., Zhu, S., and Wen, N. (2022). Deep reinforcement learning and its applications in medical imaging and radiation therapy: A survey. Phys. Med. Biol., 67. source
- Not checked yetcites [30] Adegun, 2021, Deep Learning Approach for Medical Image Analysis, Comput. Intell. Neurosci. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Using algorithms, various factors such as age, genetics, lifestyle habits, and medical history are taken into account to create comprehensive risk profiles.This enables targeted interventions and early disease surveillance.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“In addition, in [34], the authors proposed a secure edge-computing-based framework for smart health systems.This framework focuses on real-time health monitoring and ensures data security and confidentiality through clustering approaches for anomaly detection and attribute-based encryption (ABE) for secure access to biosignal data.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1.
What the sources say
- Not checked yetcites [34] Singh, 2022, Edge computing based secure health monitoring framework for electronic healthcare system, Clust. Comput. source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Using sophisticated AI algorithms, large amounts of data from multiple channels such as the scientific literature and clinical trials are subjected to careful analysis [26][27][28].This comprehensive investigation not only facilitates the identification of novel drug targets but also enables the prediction of drug efficacy.Integrating AI in this context accelerates the drug discovery process while”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
What the sources say
- Not checked yetcites [26][27][28]
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The use of edge computing facilitates the instant processing of data originating from wearable devices.This accelerates the detection of changes in a person's health status that may serve as precursors to the onset of chronic disease.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1.
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Early health prediction has great potential for infectious disease prevention and control.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“They are equipped with sensors and connectivity features that allow them to track medication use and provide feedback to patients [38,39].The use of edge computing in smart inhalers has further enhanced their functionality by enabling real-time data processing and personalized patient insights.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
What the sources say
- Not checked yetcites [38] Findair (2023, February 18). IoT with the Use of Smart Inhalers-Resources-Blog. Available online: http://findair.eu/resources/blog/iot-with-the-use-of-smart-inhalers/.
- Not checked yetcites [39] Dey, A., Haque, K.A., Nayan, A.A., and Kibria, M.G. (2020, January 28–29). IoT Based Smart Inhaler For Context-Aware Service Provisioning. Proceedings of the 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), Dhaka, Bangladesh. source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This versatile approach can be applied to various medical data analysis tasks, including image analysis, natural language processing, and time series analysis.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“On the other hand, edge computing is a distributed computing architecture that brings computing power closer to the data source [5,6,22].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
What the sources say
- Not checked yetcites [5] Shi, 2016, Edge Computing: Vision and Challenges, IEEE Internet Things J. source
- Not checked yetcites [6] Satyanarayanan, 2017, The Emergence of Edge Computing, Computer source
- Not checked yetcites [22] Montresor, 2015, Edge-centric Computing: Vision and Challenges, SIGCOMM Comput. Commun. Rev. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Smart insulin pens, used to control blood glucose levels in diabetic patients, are advanced medical devices that integrate sensors and connectivity to track insulin doses and blood glucose levels and provide real-time feedback to patients [36].These devices can process data in real time and provide patients with personalized insights through edge computing.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
What the sources say
- Not checked yetcites [36] American Diabetes Association (2023, February 18). What Is a Smart Insulin Pen?. Available online: https://diabetes.org/about-diabetes/devices-technology/smart-insulin-pen.
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Early health prediction plays a critical role in improving public health by enabling healthcare providers to identify and address potential health problems before they become more serious [11,12].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1. Introduction
What the sources say
- Not checked yetcites [11] Dong, 2021, Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care, Crit. Care source
- Not checked yetcites [12] Sepehri, 2019, An intelligent warning model for early prediction of cardiac arrest in sepsis patients, Comput. Methods Programs Biomed. source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In healthcare, edge computing holds immense transformative potential that will revolutionize early health prediction.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 2. Edge AI Overview
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Edge computing facilitates remote patient monitoring and enables healthcare providers to monitor health statuses in real time and detect changes that signal the possible onset of a chronic disease [42,43].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“By integrating edge computing, these devices are able to analyze ECG data instantly.As a result, they are able to quickly detect irregular heart rhythms and other critical abnormalities, warranting immediate medical intervention.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
What the sources say
- Not checked yetcites [37] Kamga, 2022, The Use of Wearable ECG Devices in the Clinical Setting: A Review, Curr. Emerg. Hosp. Med. Rep. source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The study results suggest that wearable devices can effectively detect, predict, and treat cardiovascular disease.However, more research is needed to improve their use.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 1.
What the sources say
- Not checked yetcites [21] Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2023). Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. Sensors, 23. source
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Edge computing is a viable option for processing data obtained from medical devices such as blood glucose meters in real time.Processing data is an indispensable component that helps detect changes in a person's health that could indicate the development of chronic diseases such as diabetes [35].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
What the sources say
- Not checked yetcites [35] Klonoff, 2017, Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things, J. Diabetes Sci. Technol. source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the implementation of these techniques depends on large volumes of carefully curated, high-quality data that require careful validation and rigorous evaluation to ensure their safety and efficacy.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 3. AI and Edge Computing Synergy in Healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The global model can be periodically updated with the latest aggregated parameters to incorporate new knowledge and improve the system's predictive capabilities.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This data diversity promotes the development of more robust and accurate predictive models by encompassing a wide range of health-related information from multiple sources.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“demonstrating an improved average AUC and a broader applicability in contrast to models trained at only one site.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.2. Early Detection of Chronic and Infectious Diseases
What the sources say
- Not checked yetcites [56] Dayan, 2021, Federated learning for predicting clinical outcomes in patients with COVID-19, Nat. Med. source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated learning facilitates continuous training of the model and adaptation to changing health conditions.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonetheless, the prudent use of AI algorithms promises to improve public health significantly.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.3. Prediction of Future Health Outcomes
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By processing data instantly and protecting sensitive information, edge computing can amplify public health outcomes by equipping healthcare providers with the insights needed to make informed judgments about an individual's health status.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By processing data at the periphery of the network and closer to its source, edge computing effectively reduces the latency that typically occurs when data are transmitted to a central data center for analysis.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With federated learning, healthcare providers can leverage a vast amount of data from a distributed patient network.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This method enables healthcare professionals to detect infectious disease outbreaks at an early stage, monitor the transmission of such diseases, and take proactive measures to contain their spread [43].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Medical Devices:
What the sources say
- Not checked yetcites [43] Jiang, 2022, Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey, Appl. Soft Comput. source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In federated learning, patient data are securely stored on edge devices such as medical devices and wearables without being transferred to a central server for storage.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Edge computing is a highly scalable framework, making it a suitable choice for health predictive initiatives that operate at scale.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Instead, the focus is on transferring aggregated model parameters, significantly reducing bandwidth requirements and computational overhead while carefully protecting the privacy of patient data.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated learning promotes collaborative research and knowledge sharing among healthcare providers by pooling anonymized, aggregated parameters, insights, and discoveries can be shared without compromising patient privacy.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This decentralized approach ensures strict data privacy and security and mitigates the risk of data breaches or violations.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By examining data from these various sources, AI algorithms provide clinicians with a comprehensive picture of an individual's well-being, allowing them to predict future health outcomes.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.3. Prediction of Future Health Outcomes
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Edge computing offers the ability to analyze large datasets in a timely manner, thereby detecting changes in a person's health status in virtually real time.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“FL facilitated rapid collaboration without data sharing and enabled a model that could be generalized across heterogeneous datasets, demonstrating the potential of FL in healthcare.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.2. Early Detection of Chronic and Infectious Diseases
What the sources say
- Not checked yetcites [56] Dayan, 2021, Federated learning for predicting clinical outcomes in patients with COVID-19, Nat. Med. source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Incorporating edge computing technology can provide significant benefits to healthcare providers by enabling rapid data analysis and informed decision making regarding a patient's health status.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated learning facilitates the creation of personalized models tailored to individual patients.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By performing data processing at the periphery of the network, edge computing can handle large amounts of data, enabling real-time monitoring of the health status of multiple individuals simultaneously.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By aggregating and combining these disparate datasets, healthcare professionals can build comprehensive models capable of capturing nuanced patterns and early health indicators, resulting in highly accurate predictions and timely interventions.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Timely detection of potential health problems is critical for early health prognosis, enabling health professionals to identify potential health problems before they worsen and become more difficult to treat.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated learning enables healthcare providers to access diverse patient data collected by wearables and medical devices.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Edge computing is a notable advancement that aims to protect sensitive data from security breaches through localized data processing at the network perimeter.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The increased cost efficiency of edge computing makes it particularly beneficial for healthcare providers, as they have the ability to leverage artificial intelligence and other cutting-edge technologies to improve healthcare outcomes.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Limiting the need for extensive data transmission over the internet provides a critical solution for early health prognosis.This attribute plays a central role in maintaining the confidentiality of an individual's private health information, ensuring that their information is used only to promote their well-being.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > •
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The risk of data breaches is significantly reduced because patients' data are stored on their devices rather than sent to a central server, which increases patient privacy [72,75].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.3.4. Federated Learning (FL)
What the sources say
- Not checked yetcites [72] Antunes, 2022, Federated Learning for Healthcare: Systematic Review and Architecture Proposal, ACM Trans. Intell. Syst. Technol. source
- Not checked yetcites [75] Ali, 2022, Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey, IEEE J. Biomed. Health Inform. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This approach allows for a more comprehensive and diverse set of data from which the model can learn, leading to greater accuracy in predicting and detecting diseases in their early stages.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 5.3.4. Federated Learning (FL)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By training local models based on their specific data, the system can tailor predictions and interventions to individual health profiles, leading to highly personalized healthcare and early detection of health problems.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As well as enhancing prediction and maintaining privacy, federated learning collaboratively trains AI models on distributed edge devices, allowing local processing for instant decision making and reduced latency.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This collective intelligence accelerates medical progress and improves early health prediction capabilities.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These technologies enable real-time data analysis, aiding health risk detection and prevention.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“The percentage of indi-viduals deployed in a combat unit was highest for the first deployment (32.7%), compared to the second (25.6%) and the third (20.8%) deploy-”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [78] Karstoft, 2023, Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models, J. Psychiatr. Res. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By leveraging the power of federated learning in scenarios where patients use wearables and various medical devices, healthcare providers can improve early prediction of health statuses, facilitate personalized interventions, and improve overall healthcare outcomes while carefully protecting patient privacy and data security.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 6. Federated Learning for Early Health Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Approximately 95% of participants were male.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [78] Karstoft, 2023, Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models, J. Psychiatr. Res. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In SMPC, each edge device i contributes its local model w i without revealing the parameters of the model or the data used for training.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Model Update:
What the sources say
- Not checked yetcites [84] Kalapaaking, A.P., Khalil, I., and Yi, X. (2023). Blockchain-based Federated Learning with SMPC Model Verification against Poisoning Attack for Healthcare Systems. arXiv. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These methods aggregate updates without disclosing sensitive information to the central server or other parties, thus maintaining privacy.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Model Update:
What the sources say
- Not checked yetcites [84] Kalapaaking, A.P., Khalil, I., and Yi, X. (2023). Blockchain-based Federated Learning with SMPC Model Verification against Poisoning Attack for Healthcare Systems. arXiv. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, Algorithm 2 incorporates a level of customization not present in Algorithm 1.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Model Update:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Averaging is the most widely used aggregation method in federated learning.
In the paper
“SMPC ensures that individual edge device data remain secret throughout the aggregation process, making it suitable for scenarios with strict privacy requirements [84].”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > Model Update:
What the sources say
- Not checked yetcites [84] Kalapaaking, A.P., Khalil, I., and Yi, X. (2023). Blockchain-based Federated Learning with SMPC Model Verification against Poisoning Attack for Healthcare Systems. arXiv. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Continuous risk models identified 55.8% of AKI cases up to 48 h early with a false positive rate of 2:1.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [71] Tomašev, N., Glorot, X., Rae, J.W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., and Protsyuk, I. (2019). Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: An AKI Case Study. Protoc. Exch. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“likely to have dementia than women. Men are more”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [65] Kavitha, 2022, Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models, Front. Public Health source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The model correctly predicted 90.2% of AKI cases requiring dialysis”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [71] Tomašev, N., Glorot, X., Rae, J.W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., and Protsyuk, I. (2019). Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: An AKI Case Study. Protoc. Exch. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The LASSO federated model outperformed the LASSO local model at three hospitals.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [91] Vaid, 2021, Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach, JMIR Med. Inform. source
- Not checked yetcites [92] Vaid, A., Jaladanki, S.K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., De Freitas, J.K., and Wanyan, T. (2020). Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19. medRxiv, Preprint. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“The proposed model improves the prediction accuracy by 13.28% and outperforms the original federated meta-learning algorithm in accuracy and speed.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [90] Chen, B., Chen, T., Zeng, X., Zhang, W., Lu, Q., Hou, Z., Zhou, J., and Helal, S. (2023). DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction. IEEE/ACM Trans. Comput. Biol. Bioinform., Online ahead of print. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Federated learning helps solve privacy concerns in machine learning; 32% of companies plan to implement federated learning.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [20] Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2023). Reviewing Federated Machine Learning and Its Use in Diseases Prediction. Sensors, 23. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Best model achieved an AUROC of 0.719.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
What the sources say
- Not checked yetcites [68] Mohammadi, 2019, Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data, AMIA Jt. Summits Transl. Sci. Proc.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper relies on this without support.
Scopus, Google Scholar, and PubMed provide comprehensive coverage of scholarly works on the subject.
“Three popular databases (Scopus, Google Scholar, and PubMed) renowned for their comprehensive coverage were used to search for scholarly works on the subject.”
The paper relies on this without support.
The search criteria C1-C4 and the 2019-2023 date restriction are adequate to identify relevant literature.
“The search strategy was based on the above search criteria.A time constraint was applied, restricting the search to include articles published between 2019 and 2023.”
The paper relies on this without support.
Edge devices have limited computational resources compared to cloud-based servers.
“Edge devices, such as smartphones, IoT devices, or edge servers, often have limited computational resources compared to cloud-based servers.”
The paper relies on this without support.
Patients are the primary users of Edge AI systems for early health prediction and are responsible for collecting and transmitting data to healthcare providers.
“Patients are the primary users of Edge AI systems for early health prediction and are responsible for collecting and transmitting data to healthcare providers.”
The paper relies on this without support.
Averaging is the most widely used aggregation method in federated learning.
“Averaging is the most widely used aggregation method in federated learning.”
The paper relies on this without support.
The final 98 references are representative of the literature on Edge AI for early health prediction.
“The final number of references eligible for this study was 98.”
The paper relies on this without support.
Edge AI relies on collecting vast amounts of personal health data, including medical records, biometric data, and lifestyle information.
“Edge AI relies on collecting vast amounts of personal health data, including medical records, biometric data, and lifestyle information.”
The paper relies on this without support.
Obtaining informed consent from individuals for data collection and usage can be challenging.
“Obtaining informed consent from individuals for data collection and usage can be challenging.”
The paper relies on this without support.
Achieving complete anonymity while maintaining data utility for accurate predictions can be a complex task.
“However, achieving complete anonymity while maintaining data utility for accurate predictions can be a complex task.”
The paper relies on this without support.
Edge AI systems store and process sensitive health data, making them potential targets for cyberattacks.
“Edge AI systems store and process sensitive health data, making them potential targets for cyberattacks.”
The paper relies on this without support.
Edge AI systems often rely on transmitting data between devices and cloud servers.
“Edge AI systems often rely on transmitting data between devices and cloud servers.”
The paper relies on this without support.
Edge AI models can be vulnerable to adversarial attacks, where malicious actors manipulate input data to deceive the system.
“Edge AI models can be vulnerable to adversarial attacks, where malicious actors manipulate input data to deceive the system.”
The paper relies on this without support.
Obtaining ground truth labels for early health prediction can be challenging.
“However, in early health prediction, obtaining ground truth labels can be challenging.”
The paper relies on this without support.
Ensuring that different devices, systems, and data sources work together is a challenge.
“Ensuring that different devices, systems, and data sources work together is a challenge.”
The paper relies on this without support.
In early health prediction, data are collected from various sources such as wearable devices, electronic health records, and sensors.
“In early health prediction, data are collected from various sources such as wearable devices, electronic health records, and sensors.”
The paper relies on this without support.
These sources often use different data formats, protocols, and standards, making it challenging to integrate and analyze data seamlessly.
“These sources often use different data formats, protocols, and standards, making it challenging to integrate and analyze data seamlessly.”
The paper relies on this without support.
Lack of interoperability can hinder the process of aggregating and fusing data from multiple sources, leading to difficulties in harmonizing and combining heterogeneous data effectively.
“However, the lack of interoperability can hinder this process, leading to difficulties in harmonizing and combining heterogeneous data effectively.”
The paper relies on this without support.
Edge AI systems rely on efficient communication between edge devices, cloud infrastructure, and central servers.
“Edge AI systems rely on efficient communication between edge devices, cloud infrastructure, and central servers.”
The paper relies on this without support.
Interoperability issues can arise due to differences in communication protocols, network connectivity, and compatibility between devices and systems.
“Interoperability issues can arise due to differences in communication protocols, network connectivity, and compatibility between devices and systems.”
The paper relies on this without support.
Interoperability can raise privacy and security concerns, especially when sensitive health data are shared or exchanged between different systems.
“Interoperability can raise privacy and security concerns, especially when sensitive health data are shared or exchanged between different systems.”
The paper relies on this without support.
Many healthcare organizations have established legacy systems and infrastructure that may not be designed to integrate with newer Edge AI technologies.
“Many healthcare organizations have established legacy systems and infrastructure that may not be designed to integrate with newer Edge AI technologies.”
The paper relies on this without support.
These legacy systems often have different data formats, protocols, and interfaces, making it difficult to seamlessly incorporate Edge AI solutions.
“These systems often have different data formats, protocols, and interfaces, making it difficult to seamlessly incorporate Edge AI solutions.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A poisoning attack on a model utilized for initial-stage disease diagnosis could modify the prediction edge for certain conditions, which is key to false-negative or -positive inpatient diagnoses [77,78].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [77]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 4.2. Source Selection and Approach
“A time constraint was applied, restricting the search to include articles published between 2019 and 2023.”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
“| [57] | 2014 | Risk predictive |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > 8. Conclusions
“| [85] | 2022 | Prediction of ICU mortality risk |”
§ Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions > References
“85. Randl, K.; Armengol, N.L.; Mondrejevski, L.; Miliou, I. Early prediction of the risk of ICU mortality with Deep Federated Learning. In Proceedings of the 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L'Aquila, Italy, 22-24 June 2023; pp. 706-711.”
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Improving in Diagnoses: Using a Novel AI Algorithm to Balance Between Accuracy and Patient Privacy
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Smart AI systems for proactive identification of latent infections in vulnerable populations
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Federated Learning for Healthcare Data Privacy and Security
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Federated learning applications in 6G communications and smart societies
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A Privacy-Preserving Decentralized AI Framework for Clinical DICOM Workflows
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Metadata
- Authors
- Badidi, E.
- Affiliations
- United Arab Emirates University (AE)
- Corresponding author
- Badidi, Elarbi
- ORCID
- Badidi, Elarbi
- Venue
- Future Internet
- Publisher
- MDPI AG
- Published
- 2023-11-18
- Volume
- 15
- Issue
- 11
- Pages
- 370
- ISSN
- 1999-5903 (ISSN-L)
- OpenAlex
- W4388796008
- Semantic Scholar
- 155b078fafb8ee9c25a6274009a315dbb26933ea
- Cited by
- 113
- References
- 96
- Citations by year
- 2026: 37 · 2025: 54 · 2024: 19 · 2023: 3
- FWCI
- 12.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Internet (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- United Arab Emirates (UAE) University (G00003443)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- edge AI, edge computing, federated learning, early disease detection, chronic disease, infectious disease surveillance, wearable devices, diabetes mellitus, cancer, electronic health records, privacy-preserving machine learning, healthcare integration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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