Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

In the paper

“The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide.”

What the sources say

• Partly supported

  The opening passage links rising prevalence, avoidable morbidity and mortality, and economic burden to a major health challenge in China. The citing sentence extends that conclusion to the world, which this passage does not establish. (LLM, conf=0.89)

  cites [4] Jia W. Diabetes care in China: Innovations and implications J. Diabetes Investig. 13 2022 1795 1797 10.1111/jdi.13908 PMC9623513 36121277 sourceExperimental — accuracy not yet measured

In the paper

“Despite the rapid progress of ML methods, there are several potential flaws, including data bias,27 overfitting,28 resource-intensive training,29 and limited transfer learning.30”

What the sources say

• ?Not checked — no full text of the sourcecites [27] Gianfrancesco M.A. Tamang S. Yazdany J. Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data JAMA Intern. Med. 178 2018 1544 1547 10.1001/jamainternmed.2018.3763 30128552 PMC6347576 source
• ?Not checked — no full text of the sourcecites [28] Ying X. An Overview of Overfitting and its Solutions J. Phys, Conf. Ser. 1168 2019 022022 source
• Supported

  Citation 29 is attached to “resource-intensive training,” not the other listed flaws. The Discussion describes training increasingly large AI and deep-learning models with massive amounts of data and scaled-up computational resources. (LLM, conf=0.88)

  cites [29] Gómez-Carmona O. Casado-Mansilla D. López-de-Ipiña D. García-Zubia J. Optimizing computational resources for edge intelligence through model cascade strategies IEEE Internet Things J. 9 2022 7404 7417 source

In the paper

“A targeted and reasonable diet can control blood sugar and lipids and supplement protein and other nutrients.65”

What the sources say

• Partly supported

  The sections “Relation between Dietary Practices and Diabetes Complications” and “Dietary Knowledge of Type 2 Diabetics” say that diet affects blood sugar and lipid levels and that nutrition therapy can help manage dyslipidemia. They do not support supplementing protein or other nutrients; the latter section instead notes that protein intake in one population exceeded recommendations. Page numbers are not available in our copy. (LLM, conf=0.87)

  cites [65] Sami W. Ansari T. Butt N.S. Ab Hamid M.R. Effect of diet on type 2 diabetes mellitus: A review Int J Health Sci-Ijh 11 2017 10.1002/dmrr.2515 PMC5426415 28539866Experimental — accuracy not yet measured

In the paper

“In summary, ML methods enable the development of AI applications that facilitate the discovery of previously unrecognized patterns in the data without the need to specify decision rules for each specific task or to account for complex interactions among input features. Therefore, ML has become the preferred framework for building AI utilities.34”

What the sources say

• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites [34] Roberts K. Boland M.R. Pruinelli L. Dcruz J. Berry A. Georgsson M. Hazen R. Sarmiento R.F. Backonja U. Yu K.-H. Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics J. Am. Med. Inf. Assoc. 24 2017 e185 e190 10.1093/jamia/ocw103 PMC6080724 27497798 sourceExperimental — accuracy not yet measured

In the paper

“With the inputs of SMBG and non-rapid insulin, this work is worth discussing today because SMBG and non-rapid insulin formulations are still used in many parts of the world.”

What the sources say

• Supported

  Section 3.1 (page not provided) describes blood-glucose measurements before and after meals and at other times as part of the study data. Section 3.2 (page not provided) identifies NPH and Ultralente insulin alongside regular insulin. The statement about why the work remains worth discussing today is the citing authors’ rationale, not a finding attributed to Fong et al. (LLM, conf=0.86)

  cites [82] Fong S. Zhang Y. Fiaidhi J. Mohammed O. Mohammed S. Evaluation of stream mining classifiers for real-time clinical decision support system: A case study of blood glucose prediction in diabetes therapy BioMed Res. Int. 2013 2013 274193 10.1155/2013/274193 PMC3791638 24163813 source

In the paper

“The prediction horizon range previously explored ranged between 5 and 180 min. Short-term predictions were the most frequently explored, as most studies used prediction horizons below 60 min.71”

What the sources say

• Supported

  The Results section under “Blood Glucose Prediction” states that prediction horizons ranged from 5 to 180 minutes and that short-term predictions were most frequent: 38 of 49 studies (76%) used horizons below 60 minutes. (LLM, conf=0.95)

  cites [71] Contreras I. Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review J. Med. Internet Res. 20 2018 e10775 10.2196/10775 PMC6000484 29848472 source

In the paper

“A proper diet is indispensable in managing diabetic patients by maintaining normal blood sugar levels and reducing the burden of pancreatic islet β cells.64”

What the sources say

• Partly supported

  The section “Certain Diet and Lifestyle May Contribute to Islet β-Cells Protection” (page unavailable) says oxidative damage can be reduced by controlling hyperglycemia and calorie intake, and discusses possible dietary contributions to β-cell protection. It does not establish that a proper diet is indispensable for diabetic patients, maintains normal blood sugar levels, or reduces β-cell burden by doing so. (LLM, conf=0.84)

  cites [64] Kitagishi Y. Nakanishi A. Minami A. Asai Y. Yasui M. Iwaizako A. Suzuki M. Ono Y. Ogura Y. Matsuda S. Certain Diet and Lifestyle May Contribute to Islet beta-cells Protection in Type-2 Diabetes via the Modulation of Cellular PI3K/AKT Pathway Open Biochem. J. 8 2014 74 82 10.2174/1874091X01408010074 25400709 PMC4231374 sourceExperimental — accuracy not yet measured

In the paper

“Prior AI models in diabetes management offered several advantages, such as data analysis, pattern recognition, decision support, and patient education, but faced limitations in natural language understanding, personalized treatment, accuracy, and potential biases.71”

What the sources say

• Partly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites [71] Contreras I. Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review J. Med. Internet Res. 20 2018 e10775 10.2196/10775 PMC6000484 29848472 sourceExperimental — accuracy not yet measured

In the paper

“User adherence is crucial to the effectiveness of DHT applications in the real world, which can be affected by convenience, user experience, and true benefits brought by this technology (for example, helping them decide when to contact their provider, being more aware of their symptoms, and feeling more connected to their provider).138,139”

What the sources say

• Partly supported

  In ‘Considering the intended users of the interventions’ (page unavailable), the paper says sustained real-world use is critical to digital technologies’ success and discusses user experience, desirable features, and reasons patients disengage. It does not specifically identify convenience as a factor or substantiate the cited sentence’s examples of deciding when to contact a provider, becoming more aware of symptoms, or feeling more connected to a provider. (LLM, conf=0.83)

  cites [138] Blakey J.D. Bender B.G. Dima A.L. Weinman J. Safioti G. Costello R.W. Digital technologies and adherence in respiratory diseases: the road ahead Eur. Respir. J. 52 2018 1801147 10.1183/13993003.01147-2018 PMC6364097 30409819 sourceExperimental — accuracy not yet measured
• Partly supported

  The Patient Themes section (page not available) reports that patients valued the app’s simplicity, symptom awareness, and connection to their provider; the discussion (p. 789) says its core features helped patients decide when to seek care. The Clinical Relevance Statement reports questionnaire adherence, but this single-app feasibility study does not establish that adherence is crucial to the real-world effectiveness of DHT applications generally, or that the listed benefits caused adherence. (LLM, conf=0.83)

  cites [139] Rudin R.S. Fanta C.H. Qureshi N. Duffy E. Edelen M.O. Dalal A.K. Bates D.W. A Clinically Integrated mHealth App and Practice Model for Collecting Patient-Reported Outcomes between Visits for Asthma Patients: Implementation and Feasibility Appl. Clin. Inf. 10 2019 783 793 10.1055/s-0039-1697597 PMC6795530 31618782 sourceExperimental — accuracy not yet measured

In the paper

“Federated computing approaches have further been developed, wherein dedicated parameter servers are responsible for aggregating and distributing local learning; however, a remainder of a central structure is kept.136”

What the sources say

• Supported

  The Concept of Swarm Learning section states the citing sentence’s claim directly: federated approaches use dedicated parameter servers to aggregate and distribute local learning, retaining a central structure. The Fig. 1 caption likewise describes federated learning as having parameter settings orchestrated by a central server. (LLM, conf=0.95)

  cites [136] Warnat-Herresthal S. Schultze H. Shastry K.L. Manamohan S. Mukherjee S. Garg V. Sarveswara R. Händler K. Pickkers P. Aziz N.A. Swarm Learning for decentralized and confidential clinical machine learning Nature 594 2021 265 270 10.1038/s41586-021-03583-3 34040261 PMC8189907 source

In the paper

“Although participants had interest in having technology to support self-management, they reported a lack of confidence with technology, as well as frustration with design features and navigation.130”

What the sources say

• Supported

  The Abstract identifies all three themes: interest in technology for self-management, lack of confidence with technology, and frustration with design features and navigation. The Results section likewise reports participants’ interest alongside lack of confidence and frustration using the apps. Page numbers were not supplied in the excerpts. (LLM, conf=0.95)

  cites [130] Sarkar U. Gourley G.I. Lyles C.R. Tieu L. Clarity C. Newmark L. Singh K. Bates D.W. Usability of Commercially Available Mobile Applications for Diverse Patients J. Gen. Intern. Med. 31 2016 1417 1426 27418347 PMC5130945 source

In the paper

“These individuals often have the poorest understanding of the disease, low use of maintenance medications, and poor health outcomes.122”

What the sources say

• Partly supported

  The Background’s health-disparities section (page not provided) identifies health literacy and medication non-adherence as factors in chronic pulmonary disease disparities. Another Background passage links poor adherence to adverse clinical outcomes. The paper does not establish that the unspecified individuals have the poorest disease understanding or specifically low use of maintenance medications. (LLM, conf=0.79)

  cites [122] Himes B.E. Weitzman E.R. Innovations in health information technologies for chronic pulmonary diseases Respir. Res. 17 2016 38 10.1186/s12931-016-0354-3 27048618 PMC4822326 sourceExperimental — accuracy not yet measured

In the paper

“Application of AI systems in the real world may lead to many unintended outcomes, including alert fatigue,131 additional burdens for clinicians, disruption of interpersonal communications, and generation of specific threats requiring increased vigilance to recognize.132”

What the sources say

• ?Not checked — no full text of the sourcecites [131] Carspecken C.W. Sharek P.J. Longhurst C. Pageler N.M. A clinical case of electronic health record drug alert fatigue: consequences for patient outcome Pediatrics 131 2013 e1970 e1973 10.1542/peds.2012-3252 23713099 source
• Partly supported

  The introduction (p. 1) says patient care information systems can add work tasks, disrupt communication routines, and create hazards requiring vigilance to detect. These points support the outcomes attached to citation 132, but the paper discusses patient care information systems rather than AI systems specifically. Alert fatigue is attached to citation 131, not this source. (LLM, conf=0.86)

  cites [132] Ash J.S. Berg M. Coiera E. Some unintended consequences of information technology in health care: The nature of patient care information system-related errors J. Am. Med. Inf. Assoc. 11 2004 104 112 10.1197/jamia.M1471 PMC353015 14633936 sourceExperimental — accuracy not yet measured

In the paper

“As an alternative, Warnat-Herresthal et al. introduced a decentralized ML approach named swarm learning,136 which dispenses with a dedicated server, shares the parameters via the swarm network, and builds the models independently on private data at the individual sites.”

What the sources say

• Supported

  The Concept of Swarm Learning section states that the authors introduce SL, which dispenses with a dedicated server, shares parameters through the Swarm network, and builds models independently on private data at individual sites. The Methods section likewise describes local training and parameter sharing without a central parameter server. (LLM, conf=0.95)

  cites [136] Warnat-Herresthal S. Schultze H. Shastry K.L. Manamohan S. Mukherjee S. Garg V. Sarveswara R. Händler K. Pickkers P. Aziz N.A. Swarm Learning for decentralized and confidential clinical machine learning Nature 594 2021 265 270 10.1038/s41586-021-03583-3 34040261 PMC8189907 source

In the paper

“To overcome this issue, Dai et al. developed an automated, interpretable, and validated system (named DeepDR) that performs real-time image quality feedback, retinal lesion detection, and early- to late-stage DR grading with high sensitivity and specificity.103”

What the sources say

• Supported

  The Discussion states that the authors developed an automated, interpretable, validated DeepDR system for real-time image quality feedback, retinal lesion detection, and early- to late-stage DR grading. It also reports high sensitivity and specificity in DR grading; the Results (Performance of the DeepDR system) give corresponding figures for mild NPDR. (LLM, conf=0.94)

  cites [103] Dai L. Wu L. Li H. Cai C. Wu Q. Kong H. Liu R. Wang X. Hou X. Liu Y. A deep learning system for detecting diabetic retinopathy across the disease spectrum Nat. Commun. 12 2021 3242 10.1038/s41467-021-23458-5 34050158 PMC8163820 source

In the paper

“In addition, two main barriers have been identified that may undermine clinicians’ enthusiasm to integrate AI systems into clinical workflows.133 First, experts may struggle to develop trust with these systems because of the numerous inputs and complex data integration involved, which can make it challenging or impossible to convey the specific logic behind an alert or recommendation. Second, some”

What the sources say

• Supported

  Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites [133] Henry K.E. Kornfield R. Sridharan A. Linton R.C. Groh C. Wang T. Wu A. Mutlu B. Saria S. Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system NPJ Digit. Med. 5 2022 97 10.1038/s41746-022-00597-7 35864312 PMC9304371 source

In the paper

“As described by the National Institutes of Health, precision medicine is an emerging disease treatment and prevention approach that considers individual variability in genes, environment, and lifestyle for each person.”

What the sources say

• Supported

  Section 3, “There is no precision medicine without AI” (page not specified), reproduces the definition and explicitly attributes it to the National Institutes of Health. It matches the citing sentence’s treatment-and-prevention approach and its reference to individual variability in genes, environment, and lifestyle. (LLM, conf=0.95)

  cites [120] Mesko B. The role of artificial intelligence in precision medicine Expert Review of Precision Medicine and Drug Development 2 2017 239 241 10.1080/23808993.2017.1380516 source

In the paper

“There exist inherent disadvantages in centralized solutions, including increased data traffic and concerns about data ownership, confidentiality, privacy, and security and creating data monopolies that favor data aggregators.135”

What the sources say

• Partly supported

  The section ‘Decentralized data and federated machine learning’ says keeping data with its owner addresses governance and ownership concerns, while the discussion of patient-data protection addresses privacy and confidentiality. Our copy does not say centralized solutions create data monopolies favoring aggregators. It also describes burdensome communication as a limitation of decentralized learning, not as increased traffic caused by centralization; no page numbers are available in the indexed passages. (LLM, conf=0.87)

  cites [135] Kaissis G.A. Makowski M.R. Rückert D. Braren R.F. Secure, privacy-preserving and federated machine learning in medical imaging Nat. Mach. Intell. 2 2020 305 311 10.1038/s42256-020-0186-1 sourceExperimental — accuracy not yet measured

In the paper

“The first well-established device was IDx-DR, approved by the FDA in 2018 for its high diagnostic performance in clinical trials.104,142”

What the sources say

• ?Not checked — no full text of the sourcecites [104] Abràmoff M.D. Lou Y. Erginay A. Clarida W. Amelon R. Folk J.C. Niemeijer M. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning Invest. Ophthalmol. Vis. Sci. 57 2016 5200 5206 10.1167/iovs.16-19964 27701631 source
• Supported

  The Discussion (page not available) identifies IDx-DR as the first FDA-authorized autonomous diagnostic AI system and says the pivotal trial results led to its authorization. The Abstract reports sensitivity of 87.2% and specificity of 90.7%, exceeding the trial’s prespecified endpoints. The paper describes FDA authorization rather than using the word “approval.” (LLM, conf=0.90)

  cites [142] Abràmoff M.D. Lavin P.T. Birch M. Shah N. Folk J.C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices NPJ Digit. Med. 1 2018 39 10.1038/s41746-018-0040-6 31304320 PMC6550188 source

In the paper

“final models, tailored in accordance with the complications, provided an accuracy up to 0.838”

What the sources say

• Partly supported

  The Abstract (page not provided) reports complication-specific final models with accuracy up to 0.838 and lists the risk factors used. The Results (page not provided) clarify that the final retained models were LR models: RF and SVM were evaluated, but the paper does not attribute that final-model accuracy to all three methods. (LLM, conf=0.88)

  cites Dagliati et al.97 Dagliati A. Marini S. Sacchi L. Cogni G. Teliti M. Tibollo V. De Cata P. Chiovato L. Bellazzi R. Machine Learning Methods to Predict Diabetes Complications J. Diabetes Sci. Technol. 12 2018 295 302 10.1177/1932296817706375 28494618 PMC5851210 sourceExperimental — accuracy not yet measured

In the paper

“the grading of DR as mild, moderate, severe, and proliferative achieves AUCs of 0.943, 0.955, 0.960, and 0.972, respectively (internal validation); in external validations, AUCs for grading range from 0.916 to 0.970”

What the sources say

• Partly supported

  Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Dai et al.103 Dai L. Wu L. Li H. Cai C. Wu Q. Kong H. Liu R. Wang X. Hou X. Liu Y. A deep learning system for detecting diabetic retinopathy across the disease spectrum Nat. Commun. 12 2021 3242 10.1038/s41467-021-23458-5 34050158 PMC8163820 sourceExperimental — accuracy not yet measured

In the paper

“the prediction accuracy was between 73% (myocardial infarction) and 83% (chronic ischemic heart disease), while the accuracy of traditional models was between 66% and 76%”

What the sources say

• Supported

  Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Ljubic et al.99 Ljubic B. Hai A.A. Stanojevic M. Diaz W. Polimac D. Pavlovski M. Obradovic Z. Predicting complications of diabetes mellitus using advanced machine learning algorithms J. Am. Med. Inf. Assoc. 27 2020 1343 1351 10.1093/jamia/ocaa120 PMC7647294 32869093 source

In the paper

“the proposed method yielded accuracy, specificity, and sensitivity of 85.27%, 83.32, and 85.24%, respectively (internal testing dataset); on a separate testing dataset, the classification accuracy, specificity, and sensitivity were 78.50%, 80.64, and 81.40%, respectively”

What the sources say

• Partly supported

  Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Huang et al.112 Huang G.M. Huang K.Y. Lee T.Y. Weng J. An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients BMC Bioinf. 16 Suppl 1 2015 S5 10.1186/1471-2105-16-S1-S5 PMC4331704 25707942 sourceExperimental — accuracy not yet measured

In the paper

“LLMs are built using natural language processing techniques and designed to recognize and understand the structure and meaning of human language, classify texts according to their content or purpose, and generate responses that are appropriate and coherent.146”

What the sources say

• Supported

  In “Using ChatGPT to write patient clinic letters,” the paper describes NLP algorithms as recognizing and understanding language, classifying texts, and generating coherent responses. The same passage says ChatGPT uses NLP technology and describes its GPT language model, supporting the citing sentence’s paraphrase about LLMs. (LLM, conf=0.88)

  cites [146] Ali S.R. Dobbs T.D. Hutchings H.A. Whitaker I.S. Using ChatGPT to write patient clinic letters Lancet. Digit. Health 5 2023 e179 e181 10.1016/S2589-7500(23)00048-1 36894409 source

In the paper

“the AUC of the system for referable DR was 0.936, sensitivity was 90.5%, and specificity was 91.6%; for vision-threatening DR, AUC was 0.958, sensitivity was 100%, and specificity was 91.1%”

What the sources say

• Supported

  The Results section reports these exact AUC, sensitivity, and specificity values for the deep learning system in the primary validation dataset. The quoted figures apply to referable and vision-threatening diabetic retinopathy, respectively. (LLM, conf=0.95)

  cites Ting et al.107 Ting D.S.W. Cheung C.Y.-L. Lim G. Tan G.S.W. Quang N.D. Gan A. Hamzah H. Garcia-Franco R. San Yeo I.Y. Lee S.Y. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes JAMA 318 2017 2211 2223 10.1001/jama.2017.18152 29234807 PMC5820739 source

In the paper

“the three-field DL system had an AUC of 0.79 in the internal validation set; assessment of the external validation set, which contained only one-field color fundus photographs, with the one-field DL system gave an AUC of 0.70”

What the sources say

• Supported

  Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites Bora et al.102 Bora A. Balasubramanian S. Babenko B. Virmani S. Venugopalan S. Mitani A. de Oliveira Marinho G. Cuadros J. Ruamviboonsuk P. Corrado G.S. Predicting the risk of developing diabetic retinopathy using deep learning Lancet. Digit. Health 3 2021 e10 e19 10.1016/S2589-7500(20)30250-8 33735063 source

In the paper

“The emergence of digital health technologies (DHTs), especially artificial intelligence (AI), may help address these obstacles and alleviate the disease burden of diabetes in the future, because AI-based DHTs in diabetes care could help implement better prevention strategies for high-risk populations, manage diabetic patients who are unable to attend physician appointments in person, deliver real-”

What the sources say

• ?Not checked — no full text of the sourcecites [8] Ashrafzadeh S. Hamdy O. Patient-Driven Diabetes Care of the Future in the Technology Era Cell Metabol. 29 2019 564 575 10.1016/j.cmet.2018.09.005 30269984 source

In the paper

“The application of ML in diabetes care and research has been widely explored in basic biomedical research,16 translational research,14 and clinical practice17 (Figure 1).”

What the sources say

• ?Not checked — no full text of the sourcecites [16] Check Hayden E. The automated lab Nature 516 2014 131 132 10.1038/516131a 25471888 source
• ?Not checked — no full text of the sourcecites [14] Yu K.-H. Beam A.L. Kohane I.S. Artificial intelligence in healthcare Nat. Biomed. Eng. 2 2018 719 731 10.1038/s41551-018-0305-z 31015651 source
• ?Not checked — no full text of the sourcecites [17] Klonoff A.N. Andy Lee W.-A. Xu N.Y. Nguyen K.T. DuBord A. Kerr D. Six Digital Health Technologies That Will Transform Diabetes J. Diabetes Sci. Technol. 17 2023 239 249 10.1177/19322968211043498 34558330 PMC9846384 source

In the paper

“Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures.”

In the paper

“However, rule-based systems are expensive to build and brittle. Furthermore, it is challenging to encode higher-order interactions between different pieces of knowledge provided by different specialists, with system effectiveness restricted by the comprehensiveness of pre-existing medical knowledge.12”

What the sources say

• ?Not checked — no full text of the sourcecites [12] Samuel A.L. Some studies in machine learning using the game of checkers (Reprinted from Journal of Research and Development, vol 3, 1959 IBM J. Res. Dev. 44 2000 206 226 10.1147/rd.441.0206 source

In the paper

“AI can predict the risk of diabetes onset and classify diabetes.”

In the paper

“One explanation for these shortcomings is that the diagnosis of diabetes is based on the measurement of only one metabolite, glucose; however, the disease is heterogeneous in terms of clinical presentation and progression.”

In the paper

“Particularly, it is worth highlighting that AI algorithms could be quite useful in times when it is difficult to manage diabetes.”

In the paper

“These AI-based models are less invasive and highly accessible, which could effectively improve the willingness of the general population to screen and provide personalized screening plans for high-risk populations.”

In the paper

“Health education aims to empower diabetic patients with increased knowledge and awareness of diabetes, which can further facilitate better disease self-management.”

In the paper

“A refined classification system could provide a powerful tool to individualize treatment regimens and identify individuals with an increased risk of complications at diagnosis.”

In the paper

“Predicting near-future BG on a minute, hour, or overnight timescale enables better diabetes management and therefore remains an active research topic.”

In the paper

“Considering that the early stages of T2D are often asymptomatic, individuals can carry the disease for years undiagnosed.48”

What the sources say

• ?Not checked — no full text of the sourcecites [48] American Diabetes Association 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes–2021 Diabetes Care 44 2021 S15 S33 33298413 source

In the paper

“These limitations have motivated the use of AI-based diagnostics that have high classification accuracy and leverage large datasets (including data from wearable and continuous monitoring devices) that are not easily interpreted.”

In the paper

“Unlike many other medications, insulin must be titrated, which means that the dose will change in response to real-time indicators such as current BG or based on what the patient has recently eaten. This process complicates insulin dosing and requires providers to perform complex calculations to determine the best dose.”

In the paper

“However, it is often poorly implemented, as clinicians lack the necessary knowledge and skills, while participants have low adherence due to design defects (e.g., prescriptions fail to take individual willingness, the appeal of exercise, and complex physical conditions into account).”

In the paper

“Clinicians provide therapeutic advice beyond insulin dosing guidance (particularly T2D), as diabetes is often associated with multiple comorbidities, leading to complex guidelines for personalized treatment, with gaps often present in which no recommendations are available.”

In the paper

“Artificial neural network approaches are the most widely applied methodology, but other ML methodologies, such as random forest and support vector machine, are being adopted with increasing frequency.”

In the paper

“Although AI can classify different types of diabetes more accurately,51,54,55 further algorithm upgrading is required to meet the clinical treatment needs.”

What the sources say

• ?Not checked — no full text of the sourcecites [51] Maniruzzaman M. Kumar N. Menhazul Abedin M. Shaykhul Islam M. Suri H.S. El-Baz A.S. Suri J.S. Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm Comput. Methods Progr. Biomed. 152 2017 23 34 10.1016/j.cmpb.2017.09.004 29054258 source
• ?Not checked — no full text of the sourcecites [54] Barakat N.H. Bradley A.P. Barakat M.N.H. Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus Ieee T Inf Technol B 14 2010 1114 1120 10.1109/Titb.2009.2039485 20071261 source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [55] Amit kumar D. Pragati A. Classification of Diabetes Mellitus Using Machine Learning Techniques Int. J. Eng. Appl. Sci. 2 2015

In the paper

“The ability to anticipate BG excursions could provide an early warning regarding ineffective or poor treatment.”

In the paper

“The prediction of diabetes onset is a part of pre-emptive medicine, accurately identifying individuals highly likely to develop diabetes at the pre-illness stage. Thus, this technology could eventually decrease the incidence of diabetes by implementing medical interventions for these people at a very early stage.”

In the paper

“Previous studies have focused on optimizing the insulin dose for both patients and clinicians.”

In the paper

“This treatment model is still worth discussing today, since most patients with diabetes do not utilize automated insulin delivery systems on account of personal preferences or relatively high costs.”

In the paper

“For clinicians, early AI-enabled decision support systems were generally designed to assist clinicians who were authoritative in the insulin dosing strategies for their patients.”

In the paper

“The control algorithm acts as a”

In the paper

“AI could be employed in understanding risk factors for diabetes onset owing to human limitations and biases when dealing with large spaces of risk factors.”

In the paper

“As with BG prediction, glycemic episode detection encompasses a set of tools that address the complexity of effective BG control. These tools enable us to detect the occurrence of glycemic episodes and provide us with the opportunity to respond promptly to their effects.”

In the paper

“Dietary monitoring is critical in patients with diabetes, especially as self-reporting of food intake is inaccurate and often impractical59; therefore, it is necessary to develop an automated solution for dietary monitoring.”

What the sources say

• ?Not checked — no full text of the sourcecites [59] Schoeller D.A. Limitations in the assessment of dietary energy intake by self-report Metabolism 44 1995 18 22 10.1016/0026-0495(95)90204-x 7869932 source

In the paper

“Treatment escalation of diabetes is another critical aspect of drug therapy strategies, which is required as a patient’s disease progresses, and multiple studies have aimed to identify optimal escalation points as an aid to clinicians.”

In the paper

“In contrast to BG prediction, most of the reviewed studies on this topic focus on detecting hyperglycemia or hypoglycemia in situations where it is not possible to monitor BG effectively. Therefore, most of these studies dealt with real-time approaches rather than predictions of future events.”

In the paper

“Current diagnostic guidelines for diabetes47 are driven by invasive measurements in clinics, possibly influenced by behavioral and ethnic factors.”

What the sources say

• ?Not checked — no full text of the sourcecites [47] American Diabetes Association 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021 Diabetes Care 44 2021 S15 S33 10.2337/dc21-S002 33298413 source

In the paper

“A challenging aspect of measuring the agreement between human expert- and AI-generated recommendations is that, even when they disagree, both can be correct, because humans (including AI developers) often express a wide variety of priorities and risk preferences.”

In the paper

“In diabetes management, the prediction of the onset of diabetes and diabetic complications would eventually decrease the incidence of diabetes and diabetic complications by implementing appropriate medical interventions for those at high risk at a very early stage.”

In the paper

“The Guardian Connect System (manufactured by Medtronic) is an example of an AI system with this functionality. This system is based on CGM, has an accompanying smartphone application,144 and was certified by the FDA in 2018.”

What the sources say

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  cites [144] Soupal J. Haskova A. Prazny M. Real-time CGM Is Superior to Flash Glucose Monitoring for Glucose Control in Type 1 Diabetes: The CORRIDA Randomized Controlled Trial Diabetes Care 43 2021 2744 2750 10.2337/dci20-0078 PMC7576432 32859607 source

In the paper

“It is characterized by using AI to predict a hypoglycemic attack an hour in advance based on the CGM data and alerts the patient.”

In the paper

“Since retinal examinations are routinely performed for patients with diabetes and the retina is a convenient window into the homeostasis of the body, it is feasible for the integration of such algorithms into DR screening programs.”

In the paper

“Previous studies have shown that applying AI in diabetes management involves all aspects of disease control, including prediction, prevention, screening, diagnosis, and treatment.”

In the paper

“Two important mechanisms of the AI domain can be used to develop decisional systems for medical predictions: a knowledge-based system that involves logic and a system based on probabilistic reasoning.125”

What the sources say

• ?Not checked — no full text of the sourcecites [125] Albu A. Stanciu L. Benefits of Using Artificial Intelligence in Medical Predictions 19–21 2015 IEEE 1 4 source

In the paper

“For example, with the growing burden of DR, AI technology can increase the productivity of existing DR tele-ophthalmology screening programs and lower unit economic costs within the community.123”

What the sources say

• ?Not checked — no full text of the sourcecites [123] Xie Y. Nguyen Q.D. Hamzah H. Lim G. Bellemo V. Gunasekeran D.V. Yip M.Y.T. Qi Lee X. Hsu W. Li Lee M. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study Lancet. Digit. Health 2 2020 e240 e249 10.1016/S2589-7500(20)30060-1 33328056 source

In the paper

“With the deployment of automated AI for specific clinical tasks, the credentials needed for diagnostic, therapeutic, supportive, and paramedical tasks need to be updated, and the roles of health-care professionals will continue to evolve as various AI modules are incorporated into the standard of care.14”

What the sources say

• ?Not checked — no full text of the sourcecites [14] Yu K.-H. Beam A.L. Kohane I.S. Artificial intelligence in healthcare Nat. Biomed. Eng. 2 2018 719 731 10.1038/s41551-018-0305-z 31015651 source

In the paper

“Integrating AI into clinical practice care could shift diabetes care toward precision, penetration, prediction, and personalization.”

In the paper

“However, several obstacles remain in the research and application of AI in diabetes management, including data quality control, poor technology design, privacy concerns, lack of clinical integration, non-adherence, and the establishment of laws and regulations (Figure 2).”

In the paper

“the results were 100% correct in determining the kind of risk (progression or development) and the risk of stroke, 90% correct in determining amputation risk, and 72.45% correct in determining the global risk and the risk of infarct”

What the sources say

• ?Not checked — no full text of the sourcecites Armengol et al.96 Armengol E. Palaudàries A. Plaza E. Individual prognosis of diabetes long-term risks: a CBR approach Methods Inf. Med. 40 2001 46 51 11310159 source

In the paper

“the results of the internal validation (T1D) reported a C index between 0.66 and 0.833, all statistically significantly different from 0.5 (p ≤ 0.0001); these results were further corroborated by the external validation (49 patients with T1D), where all models obtained similarly high and statistically significant C indexes (p < 0.05 except for the hypoglycemia and CVD model, with p = 0.0584 and 0.”

What the sources say

• ?Not checked — no full text of the sourcecites Lagani et al.94 Lagani V. Chiarugi F. Manousos D. Verma V. Fursse J. Marias K. Tsamardinos I. Realization of a service for the long-term risk assessment of diabetes-related complications J. Diabet. Complicat. 29 2015 691 698 10.1016/j.jdiacomp.2015.03.011 25953402 source

In the paper

“the AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% (>85%), specificity of 90.7% (>82.5%), and imageability rate of 96.1% for detecting the presence of more than mild DR”

What the sources say

• ?Not checked — no full text of the sourcecites Abramoff et al.104 Abràmoff M.D. Lou Y. Erginay A. Clarida W. Amelon R. Folk J.C. Niemeijer M. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning Invest. Ophthalmol. Vis. Sci. 57 2016 5200 5206 10.1167/iovs.16-19964 27701631 source

In the paper

“By addressing the challenges and capitalizing on the opportunities, AI could play a pivotal role in transforming diabetes care and improving the lives of millions of people worldwide.”

In the paper

“Despite these risks, LLMs have the potential to revolutionize patient monitoring, treatment personalization, and patient education, leading to improved outcomes and better quality of life for patients with diabetes.149,150”

What the sources say

• ?Not checked — no full text of the sourcecites [149] Stokel-Walker C. Van Noorden R. What ChatGPT and generative AI mean for science Nature 614 2023 214 216 10.1038/d41586-023-00340-6 36747115 source
• ?Not checked — no full text of the sourcecites [150] Howard A. Hope W. Gerada A. ChatGPT and antimicrobial advice: the end of the consulting infection doctor? Lancet Infect. Dis. 23 2023 405 406 10.1016/S1473-3099(23)00113-5 36822213 source

In the paper

“In conclusion, AI has the potential to optimize diabetes care by providing personalized, precise, and data-driven support to patients and health-care professionals.”

In the paper

“the system could identify different stages of DR with an average accuracy of more than 85%, a sensitivity of 82%, and a specificity of 86%”

What the sources say

• ?Not checked — no full text of the sourcecites Acharya et al.108 Acharya U.R. Lim C.M. Ng E.Y.K. Chee C. Tamura T. Computer-based detection of diabetes retinopathy stages using digital fundus images Proc. Inst. Mech. Eng. H 223 2009 545 553 10.1243/09544119jeim486 19623908 source

In the paper

“Integrating AI-based DHTs will probably become increasingly feasible in the future as technology improves, and such integration will enable new models of diabetes care.”