Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review.
| Title: | Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review. |
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| Authors: | Yismaw MB; Department of Pharmacy, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia. Electronic address: malepharm@gmai.com.; Tafere C; Department of Pharmacy, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia.; Tefera BB; Department of Pharmacy, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia.; Demsie DG; Department of Pharmacy, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia.; Feyisa K; Department of Pharmacy, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia.; Addisu ZD; Department of Pharmacy, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia.; Zeleke TK; Department of Pharmacy, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia.; Siraj EA; Department of Pharmacy, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia.; Worku MC; Department of Pharmaceutical Chemistry, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.; Berihun F; School of Medicine, College of Medicine and Health Sciences, Bahr Dar University, Bahr Dar, Ethiopia. |
| Source: | International journal of medical informatics [Int J Med Inform] 2025 Jun; Vol. 198, pp. 105858. Date of Electronic Publication: 2025 Mar 01. |
| Publication Type: | Journal Article; Systematic Review |
| Language: | English |
| Journal Info: | Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 9711057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8243 (Electronic) Linking ISSN: 13865056 NLM ISO Abbreviation: Int J Med Inform Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Shannon, Co. Clare, Ireland : Elsevier Science Ireland Ltd., c1997- |
| MeSH Terms: | Diabetes Mellitus, Type 2*/drug therapy ; Diabetes Mellitus, Type 2*/therapy ; Diabetes Mellitus, Type 2*/diagnosis ; Artificial Intelligence*; Humans |
| Abstract: | Aims: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non-adherence among patients with type 2 diabetes (T2D). Hence, this study aimed to describe and evaluate the methodological quality of AI based predictive tools for identifying T2D patients at high risk of treatment non-adherence.; Methods: A systematic search was conducted across multiple databases including, EMBASE, Cochrane Library, MedLine, and Google Scholar search. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to assess the quality of studies. The performances of tools were assessed by Area Under the Curve (AUC), precision, recall, C-index, accuracy, sensitivity, specificity or F1 score.; Results: Most studies measured predictive ability using AUC (75 %), and some only reported precision (25 %), recall (12.5 %), C-index (12.5 %), accuracy (37.5), sensitivity (12.5 %), specificity (12.5 %) or F1 score (25 %). All tools had moderate to high predictive ability (AUC > 0.70). However, only one study conducted external validation. Demographic characteristics, HbA1c, glucose monitoring data, and treatment details were typical factors used in developing tools.; Conclusions: The existing AI based tools holds significant promise for improving diabetes care. However, future studies should focus on refining the existing tools, validating in other settings, and evaluating the cost-effectiveness of AI-supported interventions.; (Copyright © 2025. Published by Elsevier B.V.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Artificial intelligence; Non-adherence; Prediction tool; Treatment; Type 2 diabetes |
| Entry Date(s): | Date Created: 20250305 Date Completed: 20250327 Latest Revision: 20250728 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.ijmedinf.2025.105858 |
| PMID: | 40043515 |
| Database: | MEDLINE |
Journal Article; Systematic Review