Scoping Review Kecerdasan Artifisial Dalam Optimasi Dosis dan Pemantauan Keamanan Obat Antidiabetik
Keywords:
Kecerdasan artifisial, optimasi dosis, obat antidiabetik, farmakovigilans, kendali glikemik
Abstract
The use of artificial intelligence in diabetes therapy for dose optimization and safety monitoring of antidiabetic drugs has increased substantially over the past decade. This scoping review was conducted to map the types of AI models applied, to evaluate their impact on glycemic control, and to analyze their contribution to strengthening pharmacovigilance systems. Approaches including machine learning, deep learning, and reinforcement learning have been implemented to model nonlinear dose–response relationships and to identify plateau effects. Adaptive dosing recommendations have been generated using clinical data and continuous glucose monitoring inputs. Improvements in time in range and reductions in HbA1c levels have been reported in comparison with conventional therapeutic approaches. In drug safety monitoring, detection and analysis of adverse drug reactions have been enhanced through the application of natural language processing, Bayesian modeling, and generative AI. Data extraction from electronic health records and individual case safety reports has been performed more efficiently and systematically. Causality assessment processes have been accelerated, leading to improved efficiency in risk evaluation. AI integration in diabetes management has also been implemented through closed-loop systems, real-time glucose prediction, and identification of patients at risk of inappropriate dosing.Several methodological and regulatory challenges remain, including data bias, limited external validation, and concerns regarding algorithmic transparency. The need for real-world validation and strengthened ethical and governance frameworks has been identified to ensure safe and accountable clinical implementation
References
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Hayakawa, T., Akimoto, H., Nagashima, T., Minagawa, K., Takahashi, Y., & Asai, S. (2025). Association between daily dose of dipeptidyl peptidase-4 inhibitors and change in glycated hemoglobin in patients with type 2 diabetes: interpretation of mixed-effects machine-learning models using electronic medical records. BMC Pharmacology and Toxicology, 26(1), 214. https://doi.org/10.1186/s40360-025-01055-2
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Jafar, A., & Pasqua, M. (2024). Postprandial glucose‐management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence. Diabetes, Obesity and Metabolism, 26(5), 1555–1566. https://doi.org/10.1111/dom.15463
Jain, A., Salas, M., Aimer, O., & Adenwala, Z. (2025). Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance. Drug Safety, 48(2), 119–127. https://doi.org/10.1007/s40264-024-01483-9
Mishra, H. P., & Gupta, R. (2025). Leveraging Generative AI for Drug Safety and Pharmacovigilance. Current Reviews in Clinical and Experimental Pharmacology, 20(2), 89–97. https://doi.org/10.2174/0127724328311400240823062829
Nagar, A., Gobburu, J., & Chakravarty, A. (2025). Artificial intelligence in pharmacovigilance: advancing drug safety monitoring and regulatory integration. Therapeutic Advances in Drug Safety, 16. https://doi.org/10.1177/20420986251361435
Nimri, R., Phillip, M., Clements, M. A., & Kovatchev, B. (2025). Closed-Loop, Artificial Intelligence-Based Decision Support Systems, and Data Science. Diabetes Technology & Therapeutics, 27(S1), S64–S78. https://doi.org/10.1089/dia.2025.8805.rev
Oh, S. H., Lee, S. J., & Park, J. (2022). Effective data-driven precision medicine by cluster-applied deep reinforcement learning. Knowledge-Based Systems, 256, 109877. https://doi.org/10.1016/j.knosys.2022.109877
Othman, Z. K., Nabashaho, P. M., Ojugbeli, E., Olpengs, D., Mustaf Ahmed, M., & Lucero-Prisno III, D. E. (2025). Advancing pharmacovigilance through artificial intelligence: A review of applications and ethical considerations. Annals of Tropical Research, 47(2), 290. https://doi.org/10.32945/atr47218.2025
Panagiotou, M., Strømmen, K., Brigato, L., de Galan, B. E., & Mougiakakou, S. (2025). Role of artificial intelligence in enhancing insulin recommendations and therapy outcomes. Die Diabetologie, 21(6), 695–703. https://doi.org/10.1007/s11428-025-01332-y
Pande, A., Kumar, A., & Anjankar, A. (2025). Harnessing artificial intelligence for theragnostic applications: Current landscape and future directions. Multidisciplinary Reviews, 8(7), 2025218. https://doi.org/10.31893/multirev.2025218
Poweleit, E. A., Vinks, A. A., & Mizuno, T. (2023). Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing. Therapeutic Drug Monitoring, 45(2), 143–150. https://doi.org/10.1097/FTD.0000000000001078
Saikia, S., Prajapati, J. B., Prajapati, B. G., Padma, V. V., & Pathak, Y. V. (2022). The Role of Artificial Intelligence in Therapeutic Drug Monitoring and Clinical Toxicity. In Recent Advances in Therapeutic Drug Monitoring and Clinical Toxicology (pp. 67–85). Springer International Publishing. https://doi.org/10.1007/978-3-031-12398-6_5
Salas, M., Petracek, J., Yalamanchili, P., Aimer, O., Kasthuril, D., Dhingra, S., Junaid, T., & Bostic, T. (2022). The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature. Pharmaceutical Medicine, 36(5), 295–306. https://doi.org/10.1007/s40290-022-00441-z
Syrowatka, A., Song, W., Amato, M. G., Foer, D., Edrees, H., Co, Z., Kuznetsova, M., Dulgarian, S., Seger, D. L., Simona, A., Bain, P. A., Purcell Jackson, G., Rhee, K., & Bates, D. W. (2022). Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. The Lancet Digital Health, 4(2), e137–e148. https://doi.org/10.1016/S2589-7500(21)00229-6
Tan, S., Lee-Barkey, Y. H., Kulzer, B., Hermanns, N., & Ehrmann, D. (2025a). Das Potenzial von künstlicher Intelligenz in der Diabetologie. Die Diabetologie, 21(6), 711–717. https://doi.org/10.1007/s11428-025-01362-6
Tan, S., Lee-Barkey, Y. H., Kulzer, B., Hermanns, N., & Ehrmann, D. (2025b). Das Potenzial von künstlicher Intelligenz in der Diabetologie. Die Diabetologie, 21(6), 711–717. https://doi.org/10.1007/s11428-025-01362-6
Tao, Y., Hou, J., Zhou, G., & Zhang, D. (2025). Artificial intelligence applied to diabetes complications: a bibliometric analysis. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1455341
Xie, T., Wang, Z., He, J., & Zhang, J. (2026). Applications of AI in the management of elderly diabetes patients. Ageing Research Reviews, 114, 102960. https://doi.org/10.1016/j.arr.2025.102960
Zale, A. D., Abusamaan, M. S., & Mathioudakis, N. (2024). Introduction to Artificial Intelligence in Diabetes. In Diabetes Digital Health, Telehealth, and Artificial Intelligence (pp. 249–261). Elsevier. https://doi.org/10.1016/B978-0-443-13244-5.00019-5
Zou, X., Liu, Y., & Ji, L. (2023). Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope? DIGITAL HEALTH, 9. https://doi.org/10.1177/20552076231203879
Aronson, J. K. (2022). Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations. Drug Safety, 45(5), 407–418. https://doi.org/10.1007/s40264-022-01156-5
Bai, B., Liu, X., & Li, H. (2026). Federated multimodal AI for precision-equitable diabetes care. Frontiers in Digital Health, 7. https://doi.org/10.3389/fdgth.2025.1678047
Barbieri, M. A., Battini, V., Carnovale, C., Cocco, M., Papoutsi, D. G., Heckmann, N. S., Dong, G., Rossi, A., Peker, S., Van Manen, R. P., Thapar, S., & Sessa, M. (2026). Artificial intelligence in pharmacovigilance signal management: a review of tools, implementations, research, and regulatory landscape. Expert Opinion on Drug Safety, 25(2), 207–222. https://doi.org/10.1080/14740338.2025.2545926
Bellapu, D., & Darwin, R. (2025). Sodium glucose co-transporter 2 inhibitors safety depending on their adverse drug reactions and glucose monitoring parameters in type 2 diabetes mellitus. Indian Journal of Pharmacology, 57(5), 329–333. https://doi.org/10.4103/ijp.ijp_924_24
Dave, R., Irfan, N., Dave, S., & Bandil, D. (2025). AI and its role in predictive preclinical models for drug efficacy testing. In Artificial Intelligence in Biomedical and Modern Healthcare Informatics (pp. 157–163). Elsevier. https://doi.org/10.1016/B978-0-443-21870-5.00015-7
Demirkol, D., Koçoğlu, F., Aktaş, Ş., & Selçukcan Erol, Ç. (2022). A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE. Journal of Istanbul Faculty of Medicine / İstanbul Tıp Fakültesi Dergisi, 85(2), 249–257. https://doi.org/10.26650/IUITFD.928111
Dimitsaki, S., Natsiavas, P., & Jaulent, M.-C. (2024). Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review. Journal of Medical Internet Research, 26, e57824. https://doi.org/10.2196/57824
Elmotia, K., Abouyaala, O., Bougrine, S., & Ouahidi, M. L. (2025). Effectiveness of AI-driven interventions in glycemic control: A systematic review and meta-analysis of randomized controlled trials. Primary Care Diabetes, 19(4), 345–354. https://doi.org/10.1016/j.pcd.2025.05.004
Ghislat, G., Hernandez-Hernandez, S., Piyawajanusorn, C., & Ballester, P. J. (2024). Data-centric challenges with the application and adoption of artificial intelligence for drug discovery. Expert Opinion on Drug Discovery, 19(11), 1297–1307. https://doi.org/10.1080/17460441.2024.2403639
Hayakawa, T., Akimoto, H., Nagashima, T., Minagawa, K., Takahashi, Y., & Asai, S. (2025). Association between daily dose of dipeptidyl peptidase-4 inhibitors and change in glycated hemoglobin in patients with type 2 diabetes: interpretation of mixed-effects machine-learning models using electronic medical records. BMC Pharmacology and Toxicology, 26(1), 214. https://doi.org/10.1186/s40360-025-01055-2
Hossmann, S., Ballhausen, H., & Rothenbühler, M. (2025). Rahmenbedingungen der künstlichen Intelligenz in der Diabetologie. Die Diabetologie, 21(6), 687–694. https://doi.org/10.1007/s11428-025-01356-4
Jacobs, P. G., Herrero, P., Facchinetti, A., Vehi, J., Kovatchev, B., Breton, M. D., Cinar, A., Nikita, K. S., Doyle, F. J., Bondia, J., Battelino, T., Castle, J. R., Zarkogianni, K., Narayan, R., & Mosquera-Lopez, C. (2024). Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Reviews in Biomedical Engineering, 17, 19–41. https://doi.org/10.1109/RBME.2023.3331297
Jafar, A., & Pasqua, M. (2024). Postprandial glucose‐management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence. Diabetes, Obesity and Metabolism, 26(5), 1555–1566. https://doi.org/10.1111/dom.15463
Jain, A., Salas, M., Aimer, O., & Adenwala, Z. (2025). Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance. Drug Safety, 48(2), 119–127. https://doi.org/10.1007/s40264-024-01483-9
Mishra, H. P., & Gupta, R. (2025). Leveraging Generative AI for Drug Safety and Pharmacovigilance. Current Reviews in Clinical and Experimental Pharmacology, 20(2), 89–97. https://doi.org/10.2174/0127724328311400240823062829
Nagar, A., Gobburu, J., & Chakravarty, A. (2025). Artificial intelligence in pharmacovigilance: advancing drug safety monitoring and regulatory integration. Therapeutic Advances in Drug Safety, 16. https://doi.org/10.1177/20420986251361435
Nimri, R., Phillip, M., Clements, M. A., & Kovatchev, B. (2025). Closed-Loop, Artificial Intelligence-Based Decision Support Systems, and Data Science. Diabetes Technology & Therapeutics, 27(S1), S64–S78. https://doi.org/10.1089/dia.2025.8805.rev
Oh, S. H., Lee, S. J., & Park, J. (2022). Effective data-driven precision medicine by cluster-applied deep reinforcement learning. Knowledge-Based Systems, 256, 109877. https://doi.org/10.1016/j.knosys.2022.109877
Othman, Z. K., Nabashaho, P. M., Ojugbeli, E., Olpengs, D., Mustaf Ahmed, M., & Lucero-Prisno III, D. E. (2025). Advancing pharmacovigilance through artificial intelligence: A review of applications and ethical considerations. Annals of Tropical Research, 47(2), 290. https://doi.org/10.32945/atr47218.2025
Panagiotou, M., Strømmen, K., Brigato, L., de Galan, B. E., & Mougiakakou, S. (2025). Role of artificial intelligence in enhancing insulin recommendations and therapy outcomes. Die Diabetologie, 21(6), 695–703. https://doi.org/10.1007/s11428-025-01332-y
Pande, A., Kumar, A., & Anjankar, A. (2025). Harnessing artificial intelligence for theragnostic applications: Current landscape and future directions. Multidisciplinary Reviews, 8(7), 2025218. https://doi.org/10.31893/multirev.2025218
Poweleit, E. A., Vinks, A. A., & Mizuno, T. (2023). Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing. Therapeutic Drug Monitoring, 45(2), 143–150. https://doi.org/10.1097/FTD.0000000000001078
Saikia, S., Prajapati, J. B., Prajapati, B. G., Padma, V. V., & Pathak, Y. V. (2022). The Role of Artificial Intelligence in Therapeutic Drug Monitoring and Clinical Toxicity. In Recent Advances in Therapeutic Drug Monitoring and Clinical Toxicology (pp. 67–85). Springer International Publishing. https://doi.org/10.1007/978-3-031-12398-6_5
Salas, M., Petracek, J., Yalamanchili, P., Aimer, O., Kasthuril, D., Dhingra, S., Junaid, T., & Bostic, T. (2022). The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature. Pharmaceutical Medicine, 36(5), 295–306. https://doi.org/10.1007/s40290-022-00441-z
Syrowatka, A., Song, W., Amato, M. G., Foer, D., Edrees, H., Co, Z., Kuznetsova, M., Dulgarian, S., Seger, D. L., Simona, A., Bain, P. A., Purcell Jackson, G., Rhee, K., & Bates, D. W. (2022). Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. The Lancet Digital Health, 4(2), e137–e148. https://doi.org/10.1016/S2589-7500(21)00229-6
Tan, S., Lee-Barkey, Y. H., Kulzer, B., Hermanns, N., & Ehrmann, D. (2025a). Das Potenzial von künstlicher Intelligenz in der Diabetologie. Die Diabetologie, 21(6), 711–717. https://doi.org/10.1007/s11428-025-01362-6
Tan, S., Lee-Barkey, Y. H., Kulzer, B., Hermanns, N., & Ehrmann, D. (2025b). Das Potenzial von künstlicher Intelligenz in der Diabetologie. Die Diabetologie, 21(6), 711–717. https://doi.org/10.1007/s11428-025-01362-6
Tao, Y., Hou, J., Zhou, G., & Zhang, D. (2025). Artificial intelligence applied to diabetes complications: a bibliometric analysis. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1455341
Xie, T., Wang, Z., He, J., & Zhang, J. (2026). Applications of AI in the management of elderly diabetes patients. Ageing Research Reviews, 114, 102960. https://doi.org/10.1016/j.arr.2025.102960
Zale, A. D., Abusamaan, M. S., & Mathioudakis, N. (2024). Introduction to Artificial Intelligence in Diabetes. In Diabetes Digital Health, Telehealth, and Artificial Intelligence (pp. 249–261). Elsevier. https://doi.org/10.1016/B978-0-443-13244-5.00019-5
Zou, X., Liu, Y., & Ji, L. (2023). Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope? DIGITAL HEALTH, 9. https://doi.org/10.1177/20552076231203879
Published
2025-12-17
How to Cite
Meilani, R., & Purwono, P. (2025, December 17). Scoping Review Kecerdasan Artifisial Dalam Optimasi Dosis dan Pemantauan Keamanan Obat Antidiabetik. Seminar Nasional Penelitian Dan Pengabdian Kepada Masyarakat 2025, 4(1), 280-288. https://doi.org/https://doi.org/10.35960/snppkm.v4i1.1423
Section
Penelitian
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