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Cited by 113IoT and Edge/Fog ComputingInternet of Things and AIArtificial Intelligence in Healthcare

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Open access
Badidi, Elarbi·2023·Future Internet
DOI 10.3390/fi15110370
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Abstract

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.

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/fi15110370

Cites

97 of 98 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 107 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    AI algorithms can analyze healthcare data from multiple sources to identify patterns and trends that may indicate disease onset.

    “AI algorithms can be used to analyze these data to identify patterns and trends that may indicate disease onset [1,2].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [1] Yu, 2018, Artificial intelligence in healthcare, Nat. Biomed. Eng. source

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    • cites [2] Malik, 2019, Overview of artificial intelligence in medicine, J. Family Med. Prim. Care source

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  • Premisecitation

    Edge computing is particularly useful in healthcare applications requiring real-time monitoring and decision making, such as wearable devices for patient monitoring and remote diagnosis and treatment.

    “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].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [7] Ray, 2019, Edge computing for Internet of Things: A survey, e-healthcare case study and future direction, J. Netw. Comput. Appl. source

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    • cites [8] Sodhro, 2019, Mobile edge computing based QoS optimization in medical healthcare applications, Int. J. Inf. Manag. source

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  • Premisecitation

    Early health prediction can be used to develop personalized interventions, treatments, and preventive measures to improve individual health and prevent onset of chronic diseases.

    “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].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [13] Hong, 2021, Wearable sensors for continuous oral cavity and dietary monitoring toward personalized healthcare and digital medicine, Analyst source

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  • Premisecitation

    Early detection of chronic diseases such as diabetes, cardiovascular disease, and cancer is one of the most important benefits of early health prediction.

    “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].”
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    • cites [14][15][16]

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  • Premisecitation

    Analysis of medical data can fine-tune drug dosages for individual patients based on their characteristics and past responses.

    “Analysis of medical data can fine-tune drug dosages for individual patients based on their characteristics and past responses [33].”
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    • cites [33] Coronato, 2020, Reinforcement learning for intelligent healthcare applications: A survey, Artif. Intell. Med. source

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    Findingcitation

    Experimental results of the proposed framework show improved performance, accuracy up to 98.5%, and data security.

    “Experimental results of the proposed framework show an improved performance, an accuracy up to 98.5%, and data security.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Singh, 2022, Edge computing based secure health monitoring framework for electronic healthcare system, Clust. Comput. source

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    Findingown data

    Further screening of the title, abstract, and full text eliminated 182 references that addressed issues far from the main topic of this review article.

    “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.”
    Quote in the paperNot a substantial claim
    • 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.

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    Findingreasoning

    The final number of references eligible for this study was 98.

    “The final number of references eligible for this study was 98.”
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    • Rests on an assumption: The final 98 references are representative of the literature on Edge AI for early health prediction.

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    Findingown data

    The total number of references, 1319, was reduced to 280 after eliminating duplicates and incomplete references.

    “However, the total number of references, 1319, was reduced to 280 after eliminating duplicates and incomplete references.”
    Quote in the paperNot a substantial claim
    • 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.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    The initial search for criteria C1-C4 found 556 references from Scopus, 565 references from Google Scholar, and 198 references from PubMed.

    “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.”
    Quote in the paperNot a substantial claim
    • 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.

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    Findingnone given

    Federated learning is substantially better than the local approach.

    “Federated learning is substantially better than the local approach.”
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    Federated learning performs equally well as the centralized approach.

    “Federated learning performs equally well as the centralized approach.”
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    Interpretationnone given

    Edge AI is quickly becoming a critical component in early health prediction.

    “Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction.”
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    Interpretationcitation

    Combining AI and edge computing allows healthcare providers to create innovative solutions that improve public health outcomes, lower healthcare costs, and improve patient experience.

    “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].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [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

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    • cites [10] Rathi, 2021, An edge AI-enabled IoT healthcare monitoring system for smart cities, Comput. Electr. Eng. source

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    Premisecitation

    Edge computing can significantly reduce latency and increase processing speeds by bringing computing power close to the data source.

    “Edge computing can significantly reduce latency and increase processing speeds by bringing computing power close to the data source.”
    Quote in the paperSubstantial claim

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    Premisecitation

    Combining health data with demographic and environmental factors allows AI to make accurate predictions about future health outcomes and develop targeted interventions to prevent disease outbreaks.

    “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].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [3] Bohr, A., and Memarzadeh, K. (2020). Artificial Intelligence in Healthcare, Academic Press. source

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    • cites [4] Rong, 2020, Artificial Intelligence in Healthcare: Review and Prediction Case Studies, Proc. Est. Acad. Sci. Eng.

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    Premisecitation

    Vital sign monitors can analyze vital signs in real time and alert healthcare providers to anomalies using edge computing.

    “Using edge computing, these devices can analyze vital signs in real time and alert healthcare providers to anomalies [40,41].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [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

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    • cites [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.

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    Premisenone given

    Wearable technology is evolving into a comprehensive data repository containing details about physical movements, heart behavior, and sleep dynamics.

    “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).”
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    Premisereasoning

    Growing privacy concerns have led to the emergence of federated learning, which enables training of models with distributed data across numerous devices without aggregating data in a central repository.

    “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.”
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    Interpretationreasoning

    This proactive approach enables healthcare providers to intervene quickly, prevent disease progression, improve health outcomes, and reduce overall healthcare spending.

    “This proactive approach enables healthcare providers to intervene quickly, prevent disease progression, improve health outcomes, and ultimately reduce overall healthcare spending.”
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    Interpretationreasoning

    Early health prediction promises to reduce healthcare costs by circumventing the need for later exhaustive and financially burdensome treatments.

    “It is worth emphasizing that early health prediction promises to reduce healthcare costs by circumventing the need for later exhaustive and financially burdensome treatments.”
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    Interpretationreasoning

    Healthcare could be significantly transformed by edge computing, which enables timely examination of significant amounts of data from wearable devices, medical instruments, and other sources in real time.

    “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.”
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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    By leveraging federated learning, physicians can harness decentralized data while protecting individual privacy, advancing predictive analytics in healthcare.

    “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.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    AI algorithms enhance healthcare providers' capabilities in making informed and accurate diagnoses and formulating personalized medical plans.

    “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.”
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    • cites [29] Topol, 2019, High-performance medicine: The convergence of human and artificial intelligence, Nat. Med. source

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    Premisereasoning

    Deep learning has been used to analyze medical images such as X-rays and MRI scans, revealing complex patterns that significantly affect diagnostic accuracy.

    “It has been used to analyze medical images such as X-rays and MRI scans, revealing complex patterns that significantly affect diagnostic accuracy.”
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    Interpretationreasoning

    The convergence of deep learning and reinforcement learning holds potential to improve analysis of medical data and lead to better treatment outcomes.

    “The convergence of deep learning and reinforcement learning holds the potential to improve the analysis of medical data, ultimately leading to better treatment outcomes.”
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    Interpretationreasoning

    Federated learning holds great promise in healthcare, where it has significant potential for predicting and forecasting disease.

    “This technique holds great promise, especially in healthcare, where it has significant potential for predicting and forecasting disease.”
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    Premisenone given

    Wearable devices equipped with sensors enable collection of vital signs, physical activity, and other health indicators, which can be processed and analyzed to facilitate timely detection of early signs of disease.

    “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.”
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    Premisecitation

    Deep learning and reinforcement learning are powerful AI techniques for medical data analysis.

    “Deep learning and reinforcement learning are two powerful AI techniques for medical data analysis [17,30].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [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

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    • cites [30] Adegun, 2021, Deep Learning Approach for Medical Image Analysis, Comput. Intell. Neurosci. source

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    Premisereasoning

    AI algorithms can create comprehensive risk profiles by taking into account factors such as age, genetics, lifestyle habits, and medical history, enabling targeted interventions and early disease surveillance.

    “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.”
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    Premisecitation

    A secure edge-computing-based framework for smart health systems focuses on real-time health monitoring and ensures data security and confidentiality through clustering for anomaly detection and attribute-based encryption.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Singh, 2022, Edge computing based secure health monitoring framework for electronic healthcare system, Clust. Comput. source

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    Premisecitation

    AI algorithms can analyze large amounts of data from scientific literature and clinical trials to identify novel drug targets and predict drug efficacy, accelerating drug discovery.

    “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”
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    • cites [26][27][28]

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    Interpretationreasoning

    Edge computing facilitates instant processing of data from wearable devices, accelerating detection of changes in health status that may serve as precursors to chronic disease.

    “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.”
    Quote in the paperSubstantial claim

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    Interpretationnone given

    Early health prediction has great potential for infectious disease prevention and control.

    “Early health prediction has great potential for infectious disease prevention and control.”
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    Premisecitation

    Smart inhalers are equipped with sensors and connectivity to track medication use and provide feedback, and edge computing enhances their functionality by enabling real-time data processing and personalized patient insights.

    “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.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [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/.

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    • cites [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

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    Premisereasoning

    Deep learning can be applied to medical data analysis tasks including image analysis, natural language processing, and time series analysis.

    “This versatile approach can be applied to various medical data analysis tasks, including image analysis, natural language processing, and time series analysis.”
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    Premisecitation

    Edge computing is a distributed computing architecture that brings computing power closer to the data source.

    “On the other hand, edge computing is a distributed computing architecture that brings computing power closer to the data source [5,6,22].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [5] Shi, 2016, Edge Computing: Vision and Challenges, IEEE Internet Things J. source

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    • cites [6] Satyanarayanan, 2017, The Emergence of Edge Computing, Computer source

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    • cites [22] Montresor, 2015, Edge-centric Computing: Vision and Challenges, SIGCOMM Comput. Commun. Rev. source

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    Premisecitation

    Smart insulin pens integrate sensors and connectivity to track insulin doses and blood glucose levels and provide real-time feedback to patients, and can process data in real time and provide personalized insights through edge computing.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

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    Premisecitation

    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.

    “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].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [11] Dong, 2021, Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care, Crit. Care source

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    • cites [12] Sepehri, 2019, An intelligent warning model for early prediction of cardiac arrest in sepsis patients, Comput. Methods Programs Biomed. source

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    Interpretationreasoning

    In healthcare, edge computing holds immense transformative potential that will revolutionize early health prediction.

    “In healthcare, edge computing holds immense transformative potential that will revolutionize early health prediction.”
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    Premisecitation

    AI in healthcare typically uses electronic health records to identify patients at risk for chronic diseases such as heart disease and diabetes.

    “AI in healthcare typically uses electronic health records to identify patients at risk for chronic diseases such as heart disease and diabetes [24,25].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [24] Mohsen, 2022, Artificial intelligence-based methods for fusion of electronic health records and imaging data, Sci. Rep. source

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    • cites [25] Briganti, 2020, Artificial Intelligence in Medicine: Today and Tomorrow, Front. Med. source

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    Premisecitation

    Edge computing facilitates remote patient monitoring and enables healthcare providers to monitor health statuses in real time and detect changes that signal possible onset of a chronic disease.

    “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].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [42] Paul, 2018, Fog Computing-Based IoT for Health Monitoring System, J. Sens. source

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    • cites [43] Jiang, 2022, Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey, Appl. Soft Comput. source

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    Premisecitation

    Wearable ECG monitors can analyze ECG data instantly by integrating edge computing, quickly detecting irregular heart rhythms and other critical abnormalities warranting immediate medical intervention.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Kamga, 2022, The Use of Wearable ECG Devices in the Clinical Setting: A Review, Curr. Emerg. Hosp. Med. Rep. source

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  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Wearable devices can effectively detect, predict, and treat cardiovascular disease, but more research is needed to improve their use.

    “The study results suggest that wearable devices can effectively detect, predict, and treat cardiovascular disease.However, more research is needed to improve their use.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [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

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    Premisecitation

    Edge computing is a viable option for processing data from medical devices such as blood glucose meters in real time, helping detect changes that could indicate development of chronic diseases such as diabetes.

    “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].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [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

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    Premisenone given

    Implementation of deep learning and reinforcement learning depends on large volumes of carefully curated, high-quality data that require validation and evaluation.

    “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.”
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    Interpretationreasoning

    The global model can be periodically updated with the latest aggregated parameters to incorporate new knowledge and improve the system's predictive capabilities.

    “The global model can be periodically updated with the latest aggregated parameters to incorporate new knowledge and improve the system's predictive capabilities.”
    Quote in the paperSubstantial claim

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    Interpretationreasoning

    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.

    “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.”
    Quote in the paperSubstantial claim

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    Premisecitation

    EXAM demonstrated improved average AUC and broader applicability compared to models trained at only one site.

    “demonstrating an improved average AUC and a broader applicability in contrast to models trained at only one site.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [56] Dayan, 2021, Federated learning for predicting clinical outcomes in patients with COVID-19, Nat. Med. source

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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisereasoning

    Federated learning facilitates continuous training of the model and adaptation to changing health conditions.

    “Federated learning facilitates continuous training of the model and adaptation to changing health conditions.”
    Quote in the paperNot a substantial claim

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    Interpretationreasoning

    The prudent use of AI algorithms promises to improve public health significantly.

    “Nonetheless, the prudent use of AI algorithms promises to improve public health significantly.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    By processing data instantly and protecting sensitive information, edge computing can amplify public health outcomes by equipping healthcare providers with insights needed to make informed judgments about an individual's health status.

    “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.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisereasoning

    Edge computing reduces latency by processing data at the periphery of the network closer to its source.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    With federated learning, healthcare providers can leverage a vast amount of data from a distributed patient network.

    “With federated learning, healthcare providers can leverage a vast amount of data from a distributed patient network.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    This method enables healthcare professionals to detect infectious disease outbreaks at an early stage, monitor transmission, and take proactive measures to contain their spread.

    “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].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [43] Jiang, 2022, Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey, Appl. Soft Comput. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisereasoning

    Edge computing is highly scalable, making it suitable for health predictive initiatives at scale.

    “Edge computing is a highly scalable framework, making it a suitable choice for health predictive initiatives that operate at scale.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Focusing on transferring aggregated model parameters significantly reduces bandwidth requirements and computational overhead while carefully protecting the privacy of patient data.

    “Instead, the focus is on transferring aggregated model parameters, significantly reducing bandwidth requirements and computational overhead while carefully protecting the privacy of patient data.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    Federated learning promotes collaborative research and knowledge sharing among healthcare providers by pooling anonymized, aggregated parameters.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    This decentralized approach ensures strict data privacy and security and mitigates the risk of data breaches or violations.

    “This decentralized approach ensures strict data privacy and security and mitigates the risk of data breaches or violations.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    By examining data from various sources, AI algorithms provide clinicians with a comprehensive picture of an individual's well-being, allowing them to predict future health outcomes.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    EXAM is a federated learning model that predicts oxygen requirements of COVID-19 patients using data from 20 institutions worldwide.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [56] Dayan, 2021, Federated learning for predicting clinical outcomes in patients with COVID-19, Nat. Med. source

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    Edge computing offers real-time data analysis, enabling detection of changes in health status in virtually real time.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    Federated learning facilitated rapid collaboration without data sharing and enabled a model that could be generalized across heterogeneous datasets, demonstrating the potential of federated learning in healthcare.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [56] Dayan, 2021, Federated learning for predicting clinical outcomes in patients with COVID-19, Nat. Med. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Incorporating edge computing can provide significant benefits to healthcare providers by enabling rapid data analysis and informed decision making regarding a patient's health status.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    Federated learning facilitates the creation of personalized models tailored to individual patients.

    “Federated learning facilitates the creation of personalized models tailored to individual patients.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisereasoning

    By performing data processing at the periphery, edge computing can handle large amounts of data, enabling real-time monitoring of multiple individuals simultaneously.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    By aggregating and combining 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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Premisenone given

    Timely detection of potential health problems is critical for early health prognosis, enabling health professionals to identify problems before they worsen.

    “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.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisereasoning

    Federated learning enables healthcare providers to access diverse patient data collected by wearables and medical devices.

    “Federated learning enables healthcare providers to access diverse patient data collected by wearables and medical devices.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisereasoning

    Edge computing protects sensitive data from security breaches through localized data processing at the network perimeter.

    “Edge computing is a notable advancement that aims to protect sensitive data from security breaches through localized data processing at the network perimeter.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    Edge computing is cost efficient, making it beneficial for healthcare providers to leverage AI and other technologies to improve healthcare outcomes.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    Limiting need for extensive data transmission over the internet provides a critical solution for early health prognosis and maintains confidentiality of private health information.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    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.

    “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].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [72] Antunes, 2022, Federated Learning for Healthcare: Systematic Review and Architecture Proposal, ACM Trans. Intell. Syst. Technol. source

      Not checked in the free run.

    • cites [75] Ali, 2022, Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey, IEEE J. Biomed. Health Inform. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Federated learning 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.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    Federated learning collaboratively trains AI models on distributed edge devices, allowing local processing for instant decision making and reduced latency.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    This collective intelligence accelerates medical progress and improves early health prediction capabilities.

    “This collective intelligence accelerates medical progress and improves early health prediction capabilities.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationnone given

    AI and edge computing technologies enable real-time data analysis, aiding health risk detection and prevention.

    “These technologies enable real-time data analysis, aiding health risk detection and prevention.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The percentage of individuals deployed in a combat unit was highest for the first deployment (32.7%), compared to the second (25.6%) and the third (20.8%) deployment.

    “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-”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    By leveraging 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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Approximately 95% of participants in the study predicting mental health problems after military deployment were male.

    “Approximately 95% of participants were male.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In SMPC, each edge device contributes its local model without revealing the model parameters or the training data.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Secure aggregation methods aggregate updates without disclosing sensitive information to the central server or other parties, thus maintaining privacy.

    “These methods aggregate updates without disclosing sensitive information to the central server or other parties, thus maintaining privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Algorithm 2 incorporates a level of customization not present in Algorithm 1.

    “Therefore, Algorithm 2 incorporates a level of customization not present in Algorithm 1.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Averaging is the most widely used aggregation method in federated learning.

  • May combine two claims.

    Premisecitation

    SMPC ensures that individual edge device data remain secret throughout the aggregation process, making it suitable for scenarios with strict privacy requirements.

    “SMPC ensures that individual edge device data remain secret throughout the aggregation process, making it suitable for scenarios with strict privacy requirements [84].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Continuous risk models identified 55.8% of acute kidney injury cases up to 48 h early with a false positive rate of 2:1.

    “Continuous risk models identified 55.8% of AKI cases up to 48 h early with a false positive rate of 2:1.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Men are more likely to have dementia than women.

    “likely to have dementia than women. Men are more”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [65] Kavitha, 2022, Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models, Front. Public Health source

      Not checked in the free run.

  • The quote only partly states this claim.

    Premisecitation

    The model correctly predicted 90.2% of acute kidney injury cases requiring dialysis within 90 days.

    “The model correctly predicted 90.2% of AKI cases requiring dialysis”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • The quote only partly states this claim.

    Premisecitation

    The LASSO federated model outperformed the LASSO local model at three hospitals for predicting 7-day mortality in hospitalized COVID-19 patients.

    “The LASSO federated model outperformed the LASSO local model at three hospitals.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [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 in the free run.

    • cites [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

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The proposed dynamic federated meta-learning model outperforms the original federated meta-learning algorithm in accuracy and speed.

    “The proposed model improves the prediction accuracy by 13.28% and outperforms the original federated meta-learning algorithm in accuracy and speed.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    32% of companies plan to implement federated learning.

    “Federated learning helps solve privacy concerns in machine learning; 32% of companies plan to implement federated learning.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • The quote only partly states this claim.

    Premisecitation

    The best model for identifying patients at risk of uncontrolled hypertension achieved an AUROC of 0.719.

    “Best model achieved an AUROC of 0.719.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [68] Mohammadi, 2019, Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data, AMIA Jt. Summits Transl. Sci. Proc.

      Not checked in the free run.

88 of 88

  • 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.”
22 of 22

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?

  • A poisoning attack on a model used for initial-stage disease diagnosis could modify the prediction edge for certain conditions, which is key to false-negative or false-positive inpatient diagnoses.

    “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]

    Section 7.1.2 (Security; page unavailable) says adversarial attacks can manipulate inputs to deceive early-health-prediction systems, and Section 7.3 (Model Bias; page unavailable) warns that erroneous predictions can have significant effects. The paper does not say that a poisoning attack changes a model’s prediction boundary or specifically causes false-negative or false-positive inpatient diagnoses. (LLM, conf=0.84)

    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.

  • Review table includes an article outside the stated search period

    Minor

    The methodology says the search was restricted to articles published from 2019 to 2023, but Table 1 includes a 2014 article. The table also lists articles from 2015 and 2018.

  • Publication year for reference [85] differs between table and reference list

    Minor

    Table 2 dates reference [85] to 2022, while its reference-list entry identifies the proceedings as 2023.

2 of 2

Cites

3 retracted

Flagged first, then by citations

Research works printed but not linked: 3

  1. Manjulatha, B., and Pabboju, S. (2021). Smart Computing Techniques and Applications, Springer.
  2. 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. · DOI · Paper page
  3. Vaughn, J., Baral, A., Vadari, M., and Boag, W. (2020, January 2–4). Dataset Bias in Diagnostic AI systems: Guidelines for Dataset Collection and Usage. Proceedings of the ACM Conference on Health, Inference and Learning, Toronto, ON, Canada.

Cited by

Sorted by citations

Related

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)
DOI
10.3390/fi15110370
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
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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

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.

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