AI-Powered Innovations in Diabetes: A Narrative Review
Main Article Content
Abstract
Artificial intelligence (AI) has been increasingly used in diabetes care with promising results of improved outcomes. This is a narrative review on the application of AI in diabetes mellitus (DM) management in Malaysia. Bibliographic search with the search terms of “diabetes mellitus”, “artificial intelligence”, “machine learning” and “Malaysia” was conducted on PubMed, Scopus, and Google Scholar. A final list of 65 publications were included for analysis in this review. Most of the studies (n=28, 43.1%) were done with the focus on DM in general. The types of AI most employed by the studies were neural network (n=15, 23.1%), supervised learning together with neural network (n=13, 20.0%), and supervised learning (n=12, 18.5%). AI was most applied in the classification (n=15, 23.1%), prediction (n=15, 23.1%), detection (n=11, 16.9%), diagnosis (n=9, 13.8%), and identification (n=7, 11.0%) of DM. High levels of accuracy, sensitivity, specificity, and precision (more than 90%) were reported in the included studies.
Downloads
Article Details
References
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. DOI: 10.1016/j.diabres.2021.109119.
Roy K, Ahmad M, Waqar K, Priyaah K, Nebhen J, Alshamrani SS, et al. An enhanced machine learning framework for type 2 diabetes classification using imbalanced data with missing values. Complexity. 2021;2021:1-21. DOI: https://doi.org/10.1155/2021/9953314.
Dzakiyullah N, Burhanuddin M, Ikram RR, Ghani K, Setyonugroho W. Machine learning methods for diabetes prediction. Int J Innov Technol Explor Eng [Internet] Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia. 2019;8:2199-205. DOI: 10.35940/ijitee.L2973.1081219
Mustapha FI, editor Current Burden of Diabetes in Malaysia. National Oral Health Research Conference; 2014; Kuala Lumpur, Malaysia.
National Health and Morbidity Survey 2019. Technical Report - Volume I. NCDs - Non-Communicable Diseases: Risk Factors and other Health Problems. Institute for Public Health, Ministry of Health, Malaysia.
Tee ES, Yap RWK. Type 2 diabetes mellitus in Malaysia: current trends and risk factors. Eur J Clin Nutr. 2017;71(7):844-9. DOI: 10.1038/ejcn.2017.44.
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40. DOI: 10.1016/j.metabol.2017.01.011.
Great Learning. What is Artificial Intelligence in 2023? Types, Trends, and Future of it? 2023. Available from: https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/.
Benko A, Lányi CS. History of artificial intelligence. Encyclopedia of Information Science and Technology, Second Edition: IGI global; 2009. p. 1759-62.
IBM. AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference? 2023. Available from: https://www.ibm.com/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks/.
Cade WT. Diabetes-related microvascular and macrovascular diseases in the physical therapy setting. Phys Ther. 2008;88(11):1322-35. DOI: 10.2522/ptj.20080008.
Aikaeli F, Njim T, Gissing S, Moyo F, Alam U, Mfinanga SG, et al. Prevalence of microvascular and macrovascular complications of diabetes in newly diagnosed type 2 diabetes in low-and-middle-income countries: A systematic review and meta-analysis. PLOS Glob Public Health. 2022;2(6):e0000599. DOI: 10.1371/journal.pgph.0000599.
Hatmal MM, Abderrahman SM, Nimer W, Al-Eisawi Z, Al-Ameer HJ, Al-Hatamleh MAI, et al. Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data. Biology (Basel). 2020;9(8). DOI: 10.3390/biology9080222.
Sim R, Chong CW, Loganadan NK, Adam NL, Hussein Z, Lee SWH. Comparison of a chronic kidney disease predictive model for type 2 diabetes mellitus in Malaysia using Cox regression versus machine learning approach. Clin Kidney J. 2023;16(3):549-59. DOI: 10.1093/ckj/sfac252.
Omar N, Nazirun NN, Vijayam B, Wahab AA, Bahuri HA. Diabetes subtypes classification for personalized health care: A review. Artificial Intelligence Review. 2023;56(3):2697-721. DOI: 10.1007/s10462-022-10202-8.
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1. DOI: 10.1186/2046-4053-4-1.
Riva JJ, Malik KM, Burnie SJ, Endicott AR, Busse JW. What is your research question? An introduction to the PICOT format for clinicians. The Journal of the Canadian Chiropractic Association. 2012;56(3):167.
EndNote X8. London: Clarivate Analytics; 2020.
JBI. Critical Appraisal Tools: Checklist for Analytical Cross Sectional Studies. Adelaide, Australia: Joanna Briggs Institute, 2024.
Migliavaca CB, Stein C, Colpani V, Munn Z, Falavigna M, Prevalence Estimates Reviews - Systematic Review Methodology G. Quality assessment of prevalence studies: a systematic review. J Clin Epidemiol. 2020;127:59-68. DOI: 10.1016/j.jclinepi.2020.06.039.
Lai PK, Teng CL, Mustapha FI. Diabetes knowledge among Malaysian adults: A scoping review and meta-analysis. Malaysian Family Physician: the Official Journal of the Academy of Family Physicians of Malaysia. 2024;19:26. DOI: https://doi.org/10.51866/rv.304
Nilashi M, Bin Ibrahim O, Mardani A, Ahani A, Jusoh A. A soft computing approach for diabetes disease classification. Health Informatics J. 2018;24(4):379-93. DOI: 10.1177/1460458216675500.
Nilashi M, Ibrahim O, Dalvi M, Ahmadi H, Shahmoradi L. Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Information and Engineering. 2017;9(3):345-57. DOI: https://doi.org/10.1016/j.fiae.2017.09.006.
Khan A, Khan A, Khan MM, Farid K, Alam MM, Su’ud MBM. Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier. Diagnostics. 2022;12(11):2595. DOI: https://doi.org/10.3390/diagnostics12112595.
Darmawan MF, Zamri A, Hatim SM, Firdaus AFA, Osman MZ. Diabetec Disease Classifier Based on Three Machine Learning Models. Mathematical Sciences and Informatics Journal. 2022;3(2):25-34. DOI: 10.24191/mij.v3i2.20118.
Al-Behadili HNK, Ku-Mahamud KR. Fuzzy unordered rule using greedy hill climbing feature selection method: An application to diabetes classification. Journal of Information and Communication Technology. 2021;20(3):391-422. DOI: https://doi.org/10.32890/jict2021.20.3.5.
Ramanathan TT, Hossen J, Sayeed S. Naïve Bayes Based Multiple Parallel Fuzzy Reasoning Method for Medical Diagnosis. J Eng Sci Technol. 2022;17(1):472-90.
Shuib L, Chiroma H. Optimization of Neural Network using Cuckoo Search for the Classification of Diabetes.
Abd Rahman MHF, Salim WWAW, Abd Wahab MF. Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset. Biological and Natural Resources Engineering Journal. 2020;3(1):48-61. DOI: https://doi.org/10.31436/cnrej.v3i1.43.
Abubakar AI, Shuib L, Chiroma H. Optimization of neural network using cuckoo search for the classification of diabetes. Journal of Computational and Theoretical Nanoscience. 2015;12(12):5755-8. DOI: https://doi.org/10.1166/jctn.2015.4713.
Haque F, Reaz MB, Chowdhury ME, Hashim FH, Arsad N, Ali SH. Diabetic sensorimotor polyneuropathy severity classification using adaptive neuro fuzzy inference system. IEEE Access. 2021;9:7618-31. DOI: https://doi.org/10.1109/ACCESS.2020.3048742.
Haque F, Bin Ibne Reaz M, Chowdhury MEH, Srivastava G, Hamid Md Ali S, Bakar AAA, et al. Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification. Diagnostics (Basel). 2021;11(5). DOI: 10.3390/diagnostics11050801.
Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibe D, Meriaudeau F. Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online. 2017;16(1):68. DOI: 10.1186/s12938-017-0352-9.
Khandakar A, Chowdhury MEH, Reaz MBI, Ali SHM, Kiranyaz S, Rahman T, et al. A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images. Sensors (Basel). 2022;22(11). DOI: 10.3390/s22114249.
Khandakar A, Chowdhury MEH, Reaz MBI, Ali SHM, Abbas TO, Alam T, et al. Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques. Sensors (Basel). 2022;22(5). DOI: 10.3390/s22051793.
Ghazali FMM, Ahmad WMAW, Srivastava KC, Shrivastava D, Noor NFM, Akbar NAN, et al. A study of creatinine level among patients with dyslipidemia and type 2 diabetes mellitus using multilayer perceptron and multiple linear regression. Journal of pharmacy & bioallied sciences. 2021;13(Suppl 1):S795. DOI: 10.4103/jpbs.JPBS_778_20.
Rajagopal A, Jha S, Alagarsamy R, Quek SG, Selvachandran G. A novel hybrid machine learning framework for the prediction of diabetes with context-customized regularization and prediction procedures. Mathematics and Computers in Simulation. 2022;198:388-406. DOI: https://doi.org/10.1016/j.matcom.2022.03.003.
Alex SA, Jhanjhi N, Humayun M, Ibrahim AO, Abulfaraj AW. Deep LSTM Model for Diabetes Prediction with Class Balancing by SMOTE. Electronics. 2022;11(17):2737. DOI: https://doi.org/10.3390/electronics11172737.
Sampathkumar A, Tesfayohani M, Shandilya SK, Goyal SB, Shaukat Jamal S, Shukla PK, et al. Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP). Comput Intell Neurosci. 2022;2022:2898061. DOI: 10.1155/2022/2898061.
Ahmed U, Issa GF, Khan MA, Aftab S, Khan MF, Said RA, et al. Prediction of diabetes empowered with fused machine learning. IEEE Access. 2022;10:8529-38. DOI: https://doi.org/10.1109/ACCESS.2022.3142097.
Sapon MA, Ismail K, Zainudin S, editors. Prediction of diabetes by using artificial neural network. Proceedings of the 2011 International Conference on Circuits, System and Simulation, Singapore; 2011.
Chowdhury MNH, Reaz MBI, Ali SHM, Crespo ML, Cicuttin A, Ahmad S, et al. Machine Learning Algorithms for Predicting the Risk of Chronic Kidney Disease in Type 1 Diabetes Patients: A Retrospective Longitudinal Study. Machine Learning Algorithms for Predicting the Risk of Chronic Kidney Disease in Type. 2023;1. DOI: https://doi.org/10.1007/s00521-024-09959-6
Sumathy B, Chakrabarty A, Gupta S, Hishan SS, Raj B, Gulati K, et al. Prediction of diabetic retinopathy using health records with machine learning classifiers and data science. International Journal of Reliable and Quality E-Healthcare (IJRQEH). 2022;11(2):1-16. DOI: 10.4018/IJRQEH.299959.
Khairudin Z, Abdul Razak NA, Abd Rahman HA, Kamarudin N, Abd Aziz NA. Prediction of diabetic retinopathy among type II diabetic patients using data mining techniques. Malaysian Journal of Computing (MJoC). 2020;5(2):572-86.
Rosli MM, Yusop NSM, Fazuly AS. Design of meal intake prediction for gestational diabetes mellitus using genetic algorithm. IAES International Journal of Artificial Intelligence. 2020;9(4):591. DOI: 10.11591/ijai.v9.i4.pp591-599.
Aibinu A, Salami M, Shafie A. Blood glucose level prediction using intelligent based modeling techniques. Inst Electrical Electron Eng. 2010;4(2):1734-7.
Akbar S, Ali H, Ahmad A, Sarker MR, Saeed A, Salwana E, et al. Prediction of Amyloid Proteins using Embedded Evolutionary & Ensemble Feature Selection based Descriptors with eXtreme Gradient Boosting Model. IEEE Access. 2023. DOI: 10.1109/ACCESS.2023.3268523.
Sonia JJ, Jayachandran P, Md AQ, Mohan S, Sivaraman AK, Tee KF. Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm. Diagnostics (Basel). 2023;13(4). DOI: 10.3390/diagnostics13040723.
Kasim S, Malek S, Aziz M, Ibrahim K. Machine learning to predict in-hospital mortality risk among heterogenous STEMI patients with diabetes. European Heart Journal. 2022;43(Supplement_1):ehab849. 176. DOI: https://doi.org/10.1093/eurheartj/ehab849.176.
Azit NA, Sahran S, Leow VM, Subramaniam M, Mokhtar S, Nawi AM. Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model. Heliyon. 2022;8(10):e10772. DOI: 10.1016/j.heliyon.2022.e10772.
Butt MM, Iskandar D, Abdelhamid SE, Latif G, Alghazo R. Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features. Diagnostics (Basel). 2022;12(7). DOI: 10.3390/diagnostics12071607.
Yap C-W. Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021;12(3):1916-22.
Albadr MAA, Ayob M, Tiun S, Al-Dhief FT, Hasan MK. Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection. Front Public Health. 2022;10:925901. DOI: 10.3389/fpubh.2022.925901.
Islam M, Ali MS, Shoumy NJ, Khatun S, Karim MSA, Bari BS. Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence. SN Applied Sciences. 2020;2:1-9. DOI: https://doi.org/10.1007/s42452-019-1884-3.
Ali MS, Khatun S, Kamarudin L, Shoumy N, Vijayasarveswari V, Islam M. Non-invasive ultra-wide band system for reliable blood glucose level detection. International Journal of Applied Engineering Research. 2016;11(14):8373-6.
Tang MCS, Teoh SS, Ibrahim H, Embong Z. Neovascularization Detection and Localization in Fundus Images Using Deep Learning. Sensors (Basel). 2021;21(16). DOI: 10.3390/s21165327.
Tang MCS, Teoh SS, Ibrahim H, Embong Z. A deep learning approach for the detection of neovascularization in fundus images using transfer learning. IEEE Access. 2022;10:20247-58. DOI: https://doi.org/10.1109/ACCESS.2022.3151644.
Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, et al. Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index. Comput Biol Med. 2017;84:59-68. DOI: 10.1016/j.compbiomed.2017.03.016.
Zulkifli MFY, Nasir NM. Classification of Electromyography Signal of Diabetes using Artificial Neural Networks. International Journal of Advanced Computer Science and Applications. 2022;13(11). DOI: 10.14569/IJACSA.2022.0131149.
Hassan HA, Yaakob M, Ismail S, Abd Rahman J, Rusni IM, Zabidi A, et al., editors. Detection of proliferative diabetic retinopathy in fundus images using convolution neural network. IOP Conference Series: Materials Science and Engineering; 2020: IOP Publishing. DOI: 10.1088/1757-899X/769/1/012029
Rahim SS, Palade V, Shuttleworth J, Jayne C. Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain Inform. 2016;3(4):249-67. DOI: 10.1007/s40708-016-0045-3.
Alasaady MT, Aris TNM, Sharef NM, Hamdan H. A proposed approach for diabetes diagnosis using neuro-fuzzy technique. Bulletin of Electrical Engineering and Informatics. 2022;11(6):3590-7. DOI: https://doi.org/10.11591/eei.v11i6.4269.
Balakrishnan V, Govindan V. An intelligent diagnostic system for diabetes using rule based reasoning and object-oriented methodology. 2011.
Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya UR. Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Comput Biol Med. 2019;113:103387. DOI: 10.1016/j.compbiomed.2019.103387.
Shakeel PM, Baskar S, Dhulipala VRS, Jaber MM. Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Health Inf Sci Syst. 2018;6(1):16. DOI: 10.1007/s13755-018-0054-0.
Abbood SH, Abdull Hamed HN, Mohd Rahim MS, Alaidi AHM, Salim ALRikabi HT. DR-LL Gan: Diabetic Retinopathy Lesions Synthesis using Generative Adversarial Network. International Journal of Online & Biomedical Engineering. 2022;18(3). DOI: https://doi.org/10.3991/ijoe.v18i03.28005.
Abbood SH, Hamed HNA, Rahim MSM, Rehman A, Saba T, Bahaj SA. Hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model. IEEE Access. 2022;10:73079-86. DOI: https://doi.org/10.1109/ACCESS.2022.3189374.
Noor FNM, Isa WHM, Majeed APA. The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline. MEKATRONIKA. 2020;2(2):62-7. DOI: https://doi.org/10.15282/mekatronika.v2i2.6769.
Kakudi HA, Loo CK, Moy FM, Masuyama N, Pasupa K. Diagnosing metabolic syndrome using genetically optimised Bayesian ARTMAP. IEEE Access. 2018;7:8437-53. DOI: https://doi.org/10.1109/ACCESS.2018.2880224.
Chowdhury NH, Reaz MBI, Haque F, Ahmad S, Ali SHM, AA AB, et al. Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients. Diagnostics (Basel). 2021;11(12). DOI: 10.3390/diagnostics11122267.
Suhaimi N, Ismail A. Comparing the Performance of Logistic Regression and Artificial Neural Networks Models: An Application to Type 2 Diabetes Mellitus. 2012.
Abdullah L. Identifying risk factors of diabetes using fuzzy inference system. IAES International Journal of Artificial Intelligence. 2017;6(4):150. DOI: 10.11591/ijai.v6.i4.pp150-158
Khandakar A, Chowdhury MEH, Ibne Reaz MB, Md Ali SH, Hasan MA, Kiranyaz S, et al. A machine learning model for early detection of diabetic foot using thermogram images. Comput Biol Med. 2021;137:104838. DOI: 10.1016/j.compbiomed.2021.104838.
Yusuf N, Zakaria A, Omar MI, Shakaff AY, Masnan MJ, Kamarudin LM, et al. In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology. BMC Bioinformatics. 2015;16(1):158. DOI: 10.1186/s12859-015-0601-5.
Ramanathan TT, Hossen MJ, Sayeed MS, Raja JE. A deep learning approach based on stochastic gradient descent and least absolute shrinkage and selection operator for identifying diabetic retinopathy. Indonesian Journal of Electrical Engineering and Computer Science. 2022;25(1):589-600. DOI: 10.11591/ijeecs.v25.i1.pp589-600.
Acharya UR, Faust O, Kadri NA, Suri JS, Yu W. Automated identification of normal and diabetes heart rate signals using nonlinear measures. Computers in biology and medicine. 2013;43(10):1523-9. DOI: https://doi.org/10.1016/j.compbiomed.2013.05.024.
Haque F, Reaz MBI, Chowdhury MEH, Ezeddin M, Kiranyaz S, Alhatou M, et al. Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait. Sensors (Basel). 2022;22(9). DOI: 10.3390/s22093507.
Chew JT, Then PHH, Sebastian Y, Raman V. Mobile Food Journalling Application with Convolutional Neural Network and Transfer Learning: A Case for Diabetes Management in Malaysia. International Journal of Advanced Computer Science and Applications. 2022;13(9):731-7. DOI: https://doi.org/10.14569/IJACSA.2022.0130986.
Chan K, Suki N. Mobile Food Recognition And Dietar Management For T2dm Patients In Malaysia. International Journal of Scientific & Technology Research. 2019;8(11):574-8.
Reza AW, Eswaran C. A decision support system for automatic screening of non-proliferative diabetic retinopathy. J Med Syst. 2011;35(1):17-24. DOI: 10.1007/s10916-009-9337-y.
Herawan T, Mohd WMW, Noraziah A. Applying Variable Precision Rough Set for Clustering Diabetics Dataset. International Journal of Multimedia and Ubiquitous Engineering. 2014;9(1):219-30. DOI: http://dx.doi.org/10.14257/ijmue.2014.9.1.21.
Lokman AS, Zain JM, Komputer F, Perisian K, editors. Designing a Chatbot for diabetic patients. International Conference on Software Engineering & Computer Systems (ICSECS'09); 2009.
Rahmat MA, Su E, Addi MM, Yeong C. GluQo: IoT-based non-invasive blood glucose monitoring. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2017;9(3-9):71-5.
Nadeem MW, Goh HG, Ponnusamy V, Andonovic I, Khan MA, Hussain M. A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes. Healthcare (Basel). 2021;9(10). DOI: 10.3390/healthcare9101393.
Butt UM, Letchmunan S, Ali M, Hassan FH, Baqir A, Sherazi HHR. Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications. J Healthc Eng. 2021;2021:9930985. DOI: 10.1155/2021/9930985.
Gholipour K, Asghari-Jafarabadi M, Iezadi S, Jannati A, Keshavarz S. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. East Mediterr Health J. 2018;24(8):770-7. DOI: 10.26719/emhj.18.012.
Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, et al. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med. 2023;4(10):101213. DOI: 10.1016/j.xcrm.2023.101213.
Klonoff DC. Personalized medicine for diabetes. J Diabetes Sci Technol. 2008;2(3):335-41. DOI: 10.1177/193229680800200301.
Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res. 2018;20(5):e10775. DOI: 10.2196/10775.
Khalifa M, Albadawy M. Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management. Computer Methods and Programs in Biomedicine Update. 2024:100141. DOI: https://doi.org/10.1016/j.cmpbup.2024.100141.
Tung AYZ, Dong LW. Malaysian Medical Students’ Attitudes and Readiness Toward AI (Artificial Intelligence): A Cross-Sectional Study. Journal of Medical Education and Curricular Development. 2023;10:23821205231201164. DOI: https://doi.org/10.1177/23821205231201164.
Li F. Research on the Legal Protection of User Data Privacy in the Era of Artificial Intelligence. Science of Law Journal. 2024;3(1):35-40. DOI: https://dx.doi.org/10.23977/law.2024.030107.
Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021;22(1):122. DOI: 10.1186/s12910-021-00687-3.
Shin Associates. Artificial Intelligence - Malaysian Legislative Framework and Key Legal Challenges. 2023. Available Online: https://www.lexology.com/library/detail.aspx?g=d9211d03-f7fe-4e5e-a0f4-b73101b6d93c.
Smith J, Doe A. Medico-legal implications of AI in diabetes management: a narrative review. J Diabetes Care [Internet]. 2024; 12(3): 45-56