This paper is in the following e-collection/theme issue:

#xPharmacologyandTherapeutics (2) Digital Health Reviews (1506) Machine Learning (1716) Artificial Intelligence (1701) Medication Adherence (117) Tools, Programs and Algorithms (320)

Published on in Vol 2, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26993, first published .
Machine Learning and Medication Adherence: Scoping Review

Machine Learning and Medication Adherence: Scoping Review

Machine Learning and Medication Adherence: Scoping Review

Authors of this article:

Aaron Bohlmann1 Author Orcid Image ;   Javed Mostafa1 Author Orcid Image ;   Manish Kumar1, 2 Author Orcid Image
Article Authors Cited by (26) Tweetations (2) Metrics

Journals

  1. Kwan Y. Peer Review of “Machine Learning and Medication Adherence: Scoping Review”. JMIRx Med 2021;2(4):e33963 View
  2. Bohlmann A, Mostafa J, Kumar M. Authors' Responses to Peer Review of “Machine Learning and Medication Adherence: Scoping Review”. JMIRx Med 2021;2(4):e33962 View
  3. Kardas P. Peer Review of “Machine Learning and Medication Adherence: Scoping Review”. JMIRx Med 2021;2(4):e33965 View
  4. Phan P, Abad C. The future of AI in health-care quality: defining the challenges and opportunities moving forward. International Journal for Quality in Health Care 2022;34(4) View
  5. Masiero M, Spada G, Sanchini V, Munzone E, Pietrobon R, Teixeira L, Valencia M, Machiavelli A, Fragale E, Pezzolato M, Pravettoni G. A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2023;12:e48852 View
  6. Yadav S, Yadav S, Verma P, Ojha S, Mishra S. Artificial Intelligence: An Advanced Evolution In Forensic and Criminal Investigation. Current Forensic Science 2022;1 View
  7. van Breugel M, Fehrmann R, Bügel M, Rezwan F, Holloway J, Nawijn M, Fontanella S, Custovic A, Koppelman G. Current state and prospects of artificial intelligence in allergy. Allergy 2023;78(10):2623 View
  8. Ekpezu A, Wiafe I, Oinas-Kukkonen H. Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review. JMIR AI 2023;2:e46779 View
  9. Georges Badr N, Khiami M, Carrubbo L, Le Dinh T. Improving access to prescription-based care through patient-centered smart pharmacy ecosystems. ITM Web of Conferences 2024;62:02003 View
  10. Rodriguez D, Chen J, Viswanadham R, Lawrence K, Mann D. Leveraging Machine Learning to Develop Digital Engagement Phenotypes of Users in a Digital Diabetes Prevention Program: Evaluation Study. JMIR AI 2024;3:e47122 View
  11. Masiero M, Spada G, Fragale E, Pezzolato M, Munzone E, Sanchini V, Pietrobon R, Teixeira L, Valencia M, Machiavelli A, Woloski R, Marzorati C, Pravettoni G. Adherence to oral anticancer treatments: network and sentiment analysis exploring perceived internal and external determinants in patients with metastatic breast cancer. Supportive Care in Cancer 2024;32(7) View
  12. Huang E, Yang J, Liao K, Tseng W, Lee C, Gill M, Compas C, See S, Tsai F. Predicting blood–brain barrier permeability of molecules with a large language model and machine learning. Scientific Reports 2024;14(1) View
  13. Dave P. Using AI to increase medication adherence. Asian Journal of Dental and Health Sciences 2024;4(2):38 View
  14. Amirthalingam P, Alatawi Y, Chellamani N, Shanmuganathan M, Ali M, Alqifari S, Mani V, Dhanasekaran M, Alqahtani A, Alanazi M, Aljabri A. Sea Horse Optimization–Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition. Sensors 2024;24(16):5224 View
  15. Liu L, Yu Z, Chen H, Gong Z, Huang X, Chen L, Fan Z, Zhang J, Yan J, Tian H, Zeng X, Chen Z, Zhang P, Zhou H. Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor. Cancer 2025;131(1) View
  16. Dave P. How AI Can Revolutionize the Pharmaceutical Industry. Journal of Drug Delivery and Therapeutics 2024;14(6):179 View
  17. Lu H, Alhaskawi A, Dong Y, Zou X, Zhou H, Ezzi S, Kota V, Hasan Abdulla Hasan Abdulla M, Abdalbary S. Patient Autonomy in Medical Education: Navigating Ethical Challenges in the Age of Artificial Intelligence. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2024;61 View
  18. Haga S. Artificial intelligence, medications, pharmacogenomics, and ethics. Pharmacogenomics 2024;25(14-15):611 View
  19. Iino H, Kizaki H, Imai S, Hori S. Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis. JMIR Formative Research 2024;8:e65882 View
  20. Kim S, Park S, Hwang H, Moon S, Park J. Effectiveness of Mobile Health Intervention in Medication Adherence: a Systematic Review and Meta-Analysis. Journal of Medical Systems 2025;49(1) View
  21. Perwitasari D, Faridah I, Dania H, Setiawan D, Safaria T, Quinn F. Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia. PLOS ONE 2025;20(1):e0315912 View
  22. Mohamad Tawpik N, Ahmad Mizher H, Zaini S. Mobile Application Intervention Effectiveness in Improving Hypertensive Patients Medication Adherence: A Systematic Review. Journal of Pharmacy 2025;5(1):115 View
  23. Alzghaibi H. Adoption barriers and facilitators of wearable health devices with AI integration: a patient-centred perspective. Frontiers in Medicine 2025;12 View
  24. Kwan H, Shell J, Fahy C, Yang S, Xing Y. Integrating Large Language Models into Medication Management in Remote Healthcare: Current Applications, Challenges, and Future Prospects. Systems 2025;13(4):281 View

Conference Proceedings

  1. Li W, Zhu W, Dorsey E, Luo J. 2023 IEEE International Conference on Digital Health (ICDH). Remote Medication Status Prediction for Individuals with Parkinson’s Disease using Time-series Data from Smartphones View
  2. Das S, Patnaik S, Pattnayak P, Mohanty A. 2023 OITS International Conference on Information Technology (OCIT). An Advancement in Forensic and Criminal Investigation Through Artificial Intelligence View