Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases
Open accessAbstract
Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.
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AI can prevent medication nonadherence using machine learning algorithms and clinical data, such as patient characteristics, medication history, disease severity, or psychosocial factors.
“Furthermore, AI can prevent medication nonadherence using machine learning algorithms and clinical data, such as patient characteristics, medication history, disease severity, or psychosocial factors. 69”
From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [69]
The “Machine Learning and Big Data Analytics” section says machine learning has been used to predict nonadherence and can help optimize adherence by combining data sources. The “Patient Characteristics” section identifies psychosocial and drug-related barriers, but the paper does not establish that algorithms using all the listed clinical-data types—including disease severity—prevent nonadherence. Page numbers are unavailable in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAI can help patients with chronic diseases, such as diabetes, hypertension, or cancer, adhere to their medication regimens by providing reminders, alerts, education, and coaching using mobile phone applications, chatbots, or wearable devices.
“AI can help patients with chronic diseases, such as diabetes, hypertension, or cancer, to adhere to their medication regimens by providing reminders, alerts, education, and coaching using mobile phone applications, chatbots, or wearable devices.69,70”
From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [69]
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Cites
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works
Interventions for enhancing medication adherence
Nieuwlaat, R., Wilczynski, N., Navarro, T., Hobson, N., Jeffery, R., Keepanasseril, A., Agoritsas, T., Mistry, N., Iorio, A., Jack, S., et al. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd000011.pub4 · Cited by 2,945 · cited 2× · see citing works
“Although pharmacist-led interventions appear to be the most effective (15), interventions are often complex, involving numerous healthcare providers and multiple components (14).”
BackgroundArtificial intelligence in medicine
Hamet, P., & Tremblay, J. (2017) · Metabolism · doi:10.1016/j.metabol.2017.01.011 · Cited by 2,334 · see citing works
Medication Adherence: WHO Cares?
Brown, M. T., & Bussell, J. K. (2011) · Mayo Clinic Proceedings · doi:10.4065/mcp.2010.0575 · Cited by 1,928 · see citing works
Bennett, J. E., Stevens, G. A., Mathers, C. D., Bonita, R., Rehm, J., Kruk, M. E., Riley, L. M., Dain, K., Kengne, A. P., Chalkidou, K., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31992-5 · Cited by 1,336 · see citing works
Medication adherence measures: An overview
Lam, W. Y., & Fresco, P. (2015) · BioMed Research International · doi:10.1155/2015/217047 · Cited by 1,308 · see citing works
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215 · see citing works
Ho, P. M., Rumsfeld, J. S., Masoudi, F. A., McClure, D. L., Plomondon, M. E., Steiner, J. F., & Magid, D. J. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.17.1836 · Cited by 1,135 · see citing works
The ethics of AI in health care: A mapping review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113172 · Cited by 1,105 · see citing works
Rasmussen, J. N., Chong, A., & Alter, D. A. (2007) · JAMA · doi:10.1001/jama.297.2.177 · Cited by 975 · see citing works
Economic impact of medication non-adherence by disease groups: a systematic review
Cutler, R. L., Fernandez-Llimos, F., Frommer, M., Benrimoj, C., & Garcia-Cardenas, V. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-016982 · Cited by 900 · cited 3× · see citing works
“As mentioned previously (55), medication non-adherence is associated with substantial increases in patient costs, so it is of utmost importance to establish whether AI and AI-assisted interventions actually increase or decrease the costs associated with non-adherence in future, well-designed…”
BackgroundInfluentialNadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619871808 · Cited by 810 · see citing works
Gellad, W. F., Grenard, J. L., & Marcum, Z. A. (2011) · The American Journal of Geriatric Pharmacotherapy · doi:10.1016/j.amjopharm.2011.02.004 · Cited by 688 · see citing works
Vollmer, S. J., Mateen, B. A., Bohner, G., Király, F. J., Ghani, R., Jonsson, P. F., Cumbers, S., Jonas, A., McAllister, K., Myles, P. R., et al. (2020) · BMJ · doi:10.1136/bmj.l6927 · Cited by 554 · see citing works
The Global Burden of Disease Study and the Preventable Burden of NCD
Benziger, C. P., Roth, G. A., & Moran, A. E. (2016) · Global Heart · doi:10.1016/j.gheart.2016.10.024 · Cited by 547 · see citing works
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
Esmaeilzadeh, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01191-1 · Cited by 507 · see citing works
Artificial Intelligence in Health Care: Bibliometric Analysis
Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020) · Journal of Medical Internet Research · doi:10.2196/18228 · Cited by 493 · see citing works
Artificial intelligence powers digital medicine
Fogel, A. L., & Kvedar, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0012-2 · Cited by 461 · see citing works
Bennett, C. C., & Hauser, K. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2012.12.003 · Cited by 378 · see citing works
Muka, T., Imo, D., Jaspers, L., Colpani, V., Chaker, L., van der Lee, S. J., Mendis, S., Chowdhury, R., Bramer, W. M., Falla, A., et al. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-014-9984-2 · Cited by 374 · see citing works
Medication adherence outcomes of 771 intervention trials: Systematic review and meta-analysis
Conn, V. S., & Ruppar, T. M. (2017) · Preventive Medicine · doi:10.1016/j.ypmed.2017.03.008 · Cited by 367 · see citing works
Assessing medication adherence: options to consider
Lehmann, A., Aslani, P., Ahmed, R. A., Dotta-Celio, J., Gauchet, A., Bedouch, P., Bugnon, O., Allenet, B., & Schneider, M. P. (2013) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-013-9865-x · Cited by 320 · see citing works
Labovitz, D. L., Shafner, L., Reyes Gil, M., Virmani, D., & Hanina, A. (2017) · Stroke · doi:10.1161/strokeaha.116.016281 · Cited by 311 · see citing works
Palmer, K., Marengoni, A., Forjaz, M. J., Jurevičienė, E., Laatikainen, T. K., Mammarella, F., Muth, C., Navickas, R., Prados‐Torres, A., Rijken, M., et al. (2017) · Health Policy · doi:10.1016/j.healthpol.2017.09.006 · Cited by 301 · see citing works
Artificial Intelligence in Medicine: Where Are We Now?
Kulkarni, S., Seneviratne, N., Baig, M. S., & Khan, A. H. A. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.001 · Cited by 289 · see citing works
Medical ethics considerations on artificial intelligence
Keskinbora, K. H. (2019) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2019.03.001 · Cited by 287 · see citing works
Systematic Review of Economic Impact Studies of Artificial Intelligence in Health Care
Wolff, J., Pauling, J. K., Keck, A., & Baumbach, J. (2019) · Journal of Medical Internet Research · doi:10.2196/16866 · Cited by 277 · see citing works
Walsh, C. A., Cahir, C., Tecklenborg, S., Byrne, C. J., Culbertson, M. A., & Bennett, K. (2019) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14075 · Cited by 273 · see citing works
Integrated care programmes for adults with chronic conditions: a meta-review
Martínez-González, N. A., Berchtold, P., Ullman, K., Busato, A., & Egger, M. (2014) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzu071 · Cited by 253 · see citing works
Nebeker, C., Torous, J., & Bartlett Ellis, R. J. (2019) · BMC Medicine · doi:10.1186/s12916-019-1377-7 · Cited by 241 · see citing works
Chaix, B., Bibault, J.-E., Pienkowski, A., Delamon, G., Guillemassé, A., Nectoux, P., & Brouard, B. (2019) · JMIR Cancer · doi:10.2196/12856 · Cited by 227 · see citing works
Participatory Design in Gerontechnology: A Systematic Literature Review
Merkel, S., & Kucharski, A. (2019) · The Gerontologist · doi:10.1093/geront/gny034 · Cited by 209 · see citing works
Kiss, P., Carcel, C., Hockham, C., & Peters, S. A. E. (2020) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcaa084 · Cited by 167 · see citing works
Bain, E., Shafner, L., Walling, D. P., Othman, A. A., Chuang‐Stein, C., Hinkle, J. E., & Hanina, A. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7030 · Cited by 163 · see citing works
Ho, P. M., Magid, D. J., Masoudi, F. A., McClure, D. L., & Rumsfeld, J. S. (2006) · BMC Cardiovascular Disorders · doi:10.1186/1471-2261-6-48 · Cited by 157 · see citing works
The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care
Rubeis, G. (2020) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2020.104186 · Cited by 139 · see citing works
Using a robot to personalise health education for children with diabetes type 1: A pilot study
Henkemans, O. B., Bierman, B. P. B., Janssen, J. B., Neerincx, M. A., Looije, R., van der Bosch, H., & van der Giessen, J. A. M. (2013) · Patient Education and Counseling · doi:10.1016/j.pec.2013.04.012 · Cited by 128 · see citing works
Kreps, G. L., & Neuhauser, L. (2013) · Patient Education and Counseling · doi:10.1016/j.pec.2013.04.014 · Cited by 126 · see citing works
Xie, Z., Clair, P. S., Goldman, D. P., & Joyce, G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212117 · Cited by 115 · see citing works
Tran, B. X., Latkin, C. A., Vu, G. T., Nguyen, H. L. T., Nghiem, S. H., Tan, M.-X., Lim, Z. K., Ho, C. S. H., & Ho, R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16152699 · Cited by 101 · see citing works
Mongkhon, P., Ashcroft, D. M., Scholfield, C. N., & Kongkaew, C. (2018) · BMJ Quality & Safety · doi:10.1136/bmjqs-2017-007453 · Cited by 99 · see citing works
Engaging diverse populations about biospecimen donation for cancer research
Dang, J. H. T., Rodriguez, E. M., Luque, J. S., Erwin, D. O., Meade, C. D., & Chen, M. S. (2014) · Journal of Community Genetics · doi:10.1007/s12687-014-0186-0 · Cited by 97 · see citing works
Randhawa, G. K., & Jackson, M. (2020) · Healthcare Management Forum · doi:10.1177/0840470419869032 · Cited by 96 · see citing works
Patient-Centered Outcomes of Medication Adherence Interventions: Systematic Review and Meta-Analysis
Conn, V. S., Ruppar, T. M., Enriquez, M., & Cooper, P. S. (2016) · Value in Health · doi:10.1016/j.jval.2015.12.001 · Cited by 83 · see citing works
Impact of Remote Consultations on Antibiotic Prescribing in Primary Health Care: Systematic Review
Han, S. M., Greenfield, G., Majeed, A., & Hayhoe, B. (2020) · Journal of Medical Internet Research · doi:10.2196/23482 · Cited by 80 · see citing works
Using Machine Learning to Examine Medication Adherence Thresholds and Risk of Hospitalization
Lo‐Ciganic, W., Donohue, J. M., Thorpe, J. M., Perera, S., Thorpe, C. T., Marcum, Z. A., & Gellad, W. F. (2015) · Medical Care · doi:10.1097/mlr.0000000000000394 · Cited by 79 · see citing works
Advancing the Science and Practice of Medication Adherence
Stirratt, M., Curtis, J., Danila, M. I., Hansen, R. A., Miller, M. J., & Gakumo, C. (2018) · Journal of general internal medicine · doi:10.1007/s11606-017-4198-4 · Cited by 77 · see citing works
Multimorbidity Patterns in the General Population: Results from the EpiChron Cohort Study
Ioakeim‐Skoufa, I., Poblador‐Plou, B., Carmona‐Pírez, J., Díez‐Manglano, J., Navickas, R., Gimeno-Feliú, L. A., González‐Rubio, F., Jurevičienė, E., Dambrauskas, L., Prados‐Torres, A., et al. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17124242 · Cited by 67 · see citing works
Cañamero, L. D., & Lewis, M. (2016) · International Journal of Social Robotics · doi:10.1007/s12369-016-0364-9 · Cited by 64 · see citing works
Accuracy of machine learning-based prediction of medication adherence in clinical research
Koesmahargyo, V., Abbas, A., Zhang, L., Guan, L., Feng, S., Yadav, V., & Galatzer‐Levy, I. R. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.113558 · Cited by 62 · see citing works
Gerontechnology and artificial intelligence: Better care for older people
Chen, L. (2020) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2020.104252 · Cited by 55 · see citing works
Eggerth, A., Hayn, D., & Schreier, G. (2019) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14045 · Cited by 54 · see citing works
Wu, X., Yang, H.-B., Yuan, R., Long, E., & Tong, R. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001055 · Cited by 53 · see citing works
Lisy, K., Campbell, J. M., Tufănaru, C., Moola, S., & Lockwood, C. S. (2018) · International Journal of Evidence-Based Healthcare · doi:10.1097/xeb.0000000000000138 · Cited by 52 · see citing works
Acceptability of Robot Assistant in Management of Type 1 Diabetes in Children
Al-Taee, M. A., Kapoor, R. R., Garrett, C. J., & Choudhary, P. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0428 · Cited by 50 · see citing works
Wang, L., Fan, R., Zhang, C., Hong, L., Zhang, T., Chen, Y., Liu, K., Wang, Z., & Zhong, J. (2020) · Patient Preference and Adherence · doi:10.2147/ppa.s253732 · Cited by 49 · see citing works
Brar Prayaga, R., Agrawal, R., Nguyen, B., Jeong, E. W., Noble, H. K., Paster, A., & Prayaga, R. S. (2019) · JMIR mHealth and uHealth · doi:10.2196/15771 · Cited by 45 · see citing works
Evaluation of an Integrated Adherence Program Aimed to Increase Medicare Part D Star Rating Measures
Leslie, R. S., Tirado, B., Patel, B. V., & Rein, P. J. (2014) · Journal of Managed Care Pharmacy · doi:10.18553/jmcp.2014.20.12.1193 · Cited by 40 · see citing works
Kim, N., McCarthy, D. E., Loh, W., Cook, J. W., Piper, M. E., Schlam, T. R., & Baker, T. B. (2019) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2019.107668 · Cited by 35 · see citing works
Long, J., Yuan, M. J., & Poonawala, R. (2016) · Interactive Journal of Medical Research · doi:10.2196/ijmr.5462 · Cited by 34 · see citing works
Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy
Torres-Robles, A., Wiecek, E., Cutler, R., Drake, B., Benrimoj, S. I., Fernandez-Llimos, F., & Garcia-Cardenas, V. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00130 · Cited by 33 · see citing works
Brar Prayaga, R., Jeong, E. W., Feger, E., Noble, H. K., Kmiec, M., & Prayaga, R. S. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8930 · Cited by 33 · see citing works
The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint
Kardas, P., Aguilar‐Palacio, I., Almada, M., Cahir, C., Costa, E. M., Giardini, A., Malo, S., Mesquida, M. M., Menditto, E., Midão, L., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/18150 · Cited by 32 · see citing works
Facing multimorbidity in the precision medicine era
Onder, G., Bernabei, R., Vetrano, D. L., Palmer, K., & Marengoni, A. (2020) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2020.111287 · Cited by 32 · see citing works
Biesen, W. V., Decruyenaere, J. M. A., Sideri, K., Cockbain, J., & Sterckx, S. (2019) · Acta Clinica Belgica · doi:10.1080/17843286.2019.1708152 · Cited by 28 · see citing works
Aziz, F., Malek, S., Ali, A. M., Wong, M., Mosleh, M. A. A., & Milow, P. (2020) · PeerJ · doi:10.7717/peerj.8286 · Cited by 28 · see citing works
On How Chronic Conditions Affect the Patient-AI Interaction: A Literature Review
Sqalli, M. T., & Al‐Thani, D. (2020) · Healthcare · doi:10.3390/healthcare8030313 · Cited by 26 · see citing works
Assessing machine learning algorithms for self-management of asthma
Kocsis, O., Arvanitis, G., Lalos, A. S., Μουστάκας, Κ., Sont, J. K., Honkoop, P., Chung, K. F., Bonini, M., Usmani, O. S., Fowler, S. J., et al. (2017) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb.2017.7995488 · Cited by 20 · see citing works
Jena, A., Singh, A. K., Kumar, P. M., Sharma, V., & Sebastian, S. (2020) · Digestive and Liver Disease · doi:10.1016/j.dld.2020.09.012 · Cited by 8 · see citing works
AI-Based Elderly Assistance Systems
Sahlab, N., & Nasser, J. (2020) · Studies in health technology and informatics · doi:10.3233/shti200633 · Cited by 7 · see citing works
Arceo-Vilas, A., Fernandez-Lozano, C., Pita, S., Pértega-Díaz, S., & Pazos, A. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.287 · Cited by 6 · see citing works
Flagged first, then by citations
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Sorted by citations
Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,152 · see citing works
“AI algorithms can also analyze patient data, such as medical history and medication usage, to provide personalized recommendations for medication management [173].”
BackgroundKhosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024) · Health Services Research and Managerial Epidemiology · doi:10.1177/23333928241234863 · Cited by 265 · see citing works
Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm
Krishnan, G., Singh, S., Pathania, M., Gosavi, S., Abhishek, S., Parchani, A., & Dhar, M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1227091 · Cited by 326 · see citing works
“AI-powered tools are also being used to monitor patient medication adherence, detecting when patients miss doses or stop taking medication (Babel et al., 2021).”
BackgroundArtificial intelligence in the field of pharmacy practice: A literature review
Chalasani, S. H., Syed, J. M., Ramesh, M., Patil, V., & Kumar, T. M. P. (2023) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2023.100346 · Cited by 209 · see citing works
The integration of AI in nursing: addressing current applications, challenges, and future directions
Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143 · see citing works
Artificial Intelligence in Orthodontics: Critical Review
Nordblom, N. F., Büttner, M., & Schwendicke, F. (2024) · Journal of dentistry research · doi:10.1177/00220345241235606 · Cited by 118 · see citing works
“The potential of following patients up remotely and thus increasing therapy adherence has been proven in other fields of medicine (Babel et al. 2021).”
BackgroundThe Role of Artificial Intelligence in Nutrition Research: A Scoping Review
Sosa-Holwerda, A., Park, O.-H., Albracht-Schulte, K., Niraula, S., Thompson, L., & Oldewage-Theron, W. (2024) · Nutrients · doi:10.3390/nu16132066 · Cited by 114 · see citing works
“…from Medicare revealed significant rates of non-adherence among patients; 76% of them were not taking their corresponding medicine, but when an AI-app was used providing daily reminders and instructions regarding the drug dosage to patients who had had a stroke, 100% adherence was achieved [62].”
BackgroundChervenak, J., Lieman, H., Blanco-Breindel, M., & Jindal, S. (2023) · Fertility and Sterility · doi:10.1016/j.fertnstert.2023.05.151 · Cited by 87 · see citing works
Nashwan, A. J., Gharib, S., Alhadidi, M. M. B., El‐Ashry, A. M., Alamgir, A., Al-Hassan, M., Khedr, M. A., Dawood, S. S., & Abufarsakh, B. (2023) · Issues in Mental Health Nursing · doi:10.1080/01612840.2023.2263579 · Cited by 82 · see citing works
“This can lead to adverse outcomes, including relapse, hospitalization, increased morbidity and mortality rates, and higher healthcare expenses (Babel et al., 2021).”
BackgroundInfluentialSu, J., Wang, Y., Liu, H., Zhang, Z., Wang, Z., & Li, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-01897-0 · Cited by 68 · see citing works
“In response to specific user requests, these systems can execute a variety of tasks such as offering personalized health advice, reminding users about timely medication intake or scheduling medical appointments 26 .”
Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions
Pham, T. D., Teh, M., Chatzopoulou, D., Holmes, S., & Coulthard, P. (2024) · Current Oncology · doi:10.3390/curroncol31090389 · Cited by 70 · see citing works
“In addition to tracking disease recurrence, AI plays a crucial role in monitoring patient compliance with treatment regimens [136–138].”
BackgroundOmoteso, O. A., Fadaka, A. O., Walker, R. B., & Khamanga, S. M. (2025) · Microorganisms · doi:10.3390/microorganisms13040722 · Cited by 63 · see citing works
“By identifying individuals at risk of non-adherence, healthcare providers can proactively intervene to enhance compliance [220,224].”
Kanyongo, W., & Ezugwu, A. E.-S. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101232 · Cited by 67 · see citing works
Kardas, P., Bennett, B., Borah, B., Burnier, M., Daly, C. J., Hiligsmann, M., Menditto, E., Peterson, A. M., Slejko, J., Tóth, K., et al. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1444012 · Cited by 47 · see citing works
The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease
Chowdhury, M. A., Rizk, R., Chiu, C., Zhang, J., Scholl, J. L., Bosch, T. J., Singh, A. P., Baugh, L. A., McGough, J. S., Santosh, K., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13020427 · Cited by 57 · see citing works
“Furthermore, AI/ML applications are being actively investigated in patient health management for the outpatient monitoring of medication adherence and drug-related toxicities and side effects [161,162].”
Khude, H., & Shende, P. K. (2025) · Advances in Integrative Medicine · doi:10.1016/j.aimed.2025.100529 · Cited by 57 · see citing works
The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review
Milasan, L. H., & Scott, D. (2025) · International Journal of Mental Health Nursing · doi:10.1111/inm.70003 · Cited by 47 · see citing works
Reis, Z. S. N., Pereira, G. M. V., Dias, C. D. S., Lage, E. M., de Oliveira, I. J. R., & Pagano, A. S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1523070 · Cited by 42 · see citing works
Lu, H. J., Alhaskawi, A., Dong, Y., Zou, X., Zhou, H., Ezzi, S. H. A., Kota, V. G., Abdulla, M. H. A. H., & Abdalbary, S. A. (2024) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580241266364 · Cited by 38 · see citing works
“Babel et al 83 discussed the implementation of AI-powered tools such as mobile applications and reminder systems that not only support medication adherence but also empower patients by providing personalized health information and feedback.”
BackgroundTraylor, D., Kern, K. V., Anderson, E. E., & Henderson, R. (2025) · Cureus · doi:10.7759/cureus.76825 · Cited by 30 · see citing works
Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care
Rakers, M. M., van Buchem, M. M., Kucenko, S., de Hond, A., Kant, I. M. J., van Smeden, M., Moons, K. G. M., Leeuwenberg, A., Chavannes, N. H., Villalobos-Quesada, M. J., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.32990 · Cited by 36 · see citing works
Baron, R., & Haick, H. (2024) · ACS Sensors · doi:10.1021/acssensors.4c00636 · Cited by 36 · see citing works
Integrating Artificial Intelligence in the Diagnosis of COPD Globally: A Way Forward.
Robertson, N., Centner, C. S., & Siddharthan, T. (2023) · Chronic obstructive pulmonary diseases · doi:10.15326/jcopdf.2023.0449 · Cited by 31
“AI has primarily been used primarily infectious disease monitoring and management in LMICs with increasing use in non-communicable disease interventions such as diabetes and cancer [23-26].”
Antoun, I., Abdelrazik, A., Eldesouky, M., Li, X., Layton, G. R., Zakkar, M., Somani, R., & Ng, G. A. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1596574 · Cited by 33 · see citing works
Villarreal‐Zegarra, D., Reátegui-Rivera, C. M., García-Serna, J., Quispe-Callo, G., Lázaro-Cruz, G., Centeno-Terrazas, G., Gálvez-Arévalo, R. A., Escobar-Agreda, S., Rodríguez, A. D., & Finkelstein, J. (2024) · JMIR Mental Health · doi:10.2196/59560 · Cited by 36 · see citing works
“There is a need to evaluate the implementation and promotion of these interventions as part of mental health strategies because this could be an effective strategy to reduce depressive and anxiety symptoms in health service users [60,61].”
BackgroundYue, O., Wang, X., Long, X., Bai, Z., Zou, X., & Liu, X. (2024) · Advanced Science · doi:10.1002/advs.202307369 · Cited by 33 · see citing works
Hurvitz, N., & Ilan, Y. (2023) · Clinics and Practice · doi:10.3390/clinpract13040089 · Cited by 32 · see citing works
Okeibunor, J., Jaca, A., Iwu, C. J., Idemili-Aronu, N., Ba, H., Zantsi, Z. P., Ndlambe, A. M., Mavundza, E. J., Muneene, D., Wiysonge, C. S., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1102185 · Cited by 32 · see citing works
“Frontiers in PublicHealth 03 frontiersin.org FIGURE 1 Study selection process for the scoping review. f. Artificial neural network: Two studies investigated the use of artificial neural network (ANN) to predict infectious disease (COVID-19) and non-infectious disease (hearing loss) among noise-exposed workers (72, 78)…”
MethodInfluentialAlsanosi, S., & Padmanabhan, S. (2024) · Healthcare · doi:10.3390/healthcare12070788 · Cited by 31 · see citing works
“These tools can send reminders to healthcare providers and patients to track their medication intake and provide feedback, thereby encouraging better adherence and reducing the chances of problems associated with polypharmacy [53].”
BackgroundRammal, D. S., Alomar, M. J., & Palaian, S. (2024) · Pharmacy Practice · doi:10.18549/pharmpract.2024.2.2958 · Cited by 25 · see citing works
Cirstea, N., Radu, A.-F., Vesa, C. M., Radu, A. F., Bungău, A. F., Ţiţ, D. M., Nistor-Cseppento, C. D., Tarce, A. G., & BUNGAU, S. G. (2024) · Cureus · doi:10.7759/cureus.69764 · Cited by 21 · see citing works
Farhana, L., Rahayu, F. P., Sholihah, S., Sweileh, W., Abdulah, R., & Alfian, S. D. (2025) · Patient Preference and Adherence · doi:10.2147/ppa.s530503 · Cited by 22 · see citing works
“94 95–98 Meanwhile, blockchain supported patient-centered healthcare by improving data integrity, transparency, security, and interoperability.”
Medication Adherence and Intervention Strategies: Why Should We Care
Schnorrerova, P., Matalová, P., & Wawruch, M. (2025) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.1007/s44411-025-00227-0 · Cited by 21 · see citing works
“These tools encompass a diverse range of technologies, strategies, and resources designed to support, screen, and monitor patient adherence [61, 62].”
Feng, G., Weng, F., Lu, W. P., Xu, L., Zhu, W., Tan, M., & Weng, P. (2025) · International Journal of General Medicine · doi:10.2147/ijgm.s516247 · Cited by 28 · see citing works
“32 Computer vision applications have achieved outstanding results in fall detection sensitivity (92”
Bus, S., Reeves, N. D., Armstrong, D. G., & Najafi, B. (2024) · Diabetes/Metabolism Research Reviews · doi:10.1002/dmrr.3769 · Cited by 22 · see citing works
“38–40 For instance, a machine learning algorithm could analyse patterns in a patient's offloading adherence and predict any deviations from the recommended strategy.”
BackgroundKanyongo, W., & Ezugwu, A. E.-S. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101210 · Cited by 30 · see citing works
Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care
Tan, L.-R. D., Nguyen, N., López, E., Peverini, D., Shedd, M., Alismail, A., & Nguyen, H. B. (2025) · Journal of Asthma and Allergy · doi:10.2147/jaa.s535264 · Cited by 20 · see citing works
“17 According to Chan et al, medication adherence is below ideal, averaging about 50% and declining significantly more in high-risk groups.”
InfluentialVaismoradi, M., Rae, J., Turunen, H. E., & Logan, P. (2024) · Digital Health · doi:10.1177/20552076241287272 · Cited by 21 · see citing works
The Role of Virtual Triage in Improving Clinician Experience and Satisfaction: A Narrative Review
Gellert, G. A., Rasławska-Socha, J., Marcjasz, N., Price, T. J., Heyduk, A., Młodawska, A., Kuszczyński, K., Jędruch, A., & Orzechowski, P. D. (2023) · Telemedicine Reports · doi:10.1089/tmr.2023.0020 · Cited by 22 · see citing works
Sekandi, J., Shi, W.-L., Zhu, R.-H., Kaggwa, P., Mwebaze, E., & Li, S. (2022) · JMIR AI · doi:10.2196/40167 · Cited by 20 · see citing works
“Our pilot work builds on the existing literature and aspiration to expand the use of AI in routine health care [43] and, specifically, medication adherence monitoring [3].”
Data Ownership in the AI-Powered Integrative Health Care Landscape
Liu, S., & Guo, L. (2024) · JMIR Medical Informatics · doi:10.2196/57754 · Cited by 27 · see citing works
Gackowski, M., Jasińska-Stroschein, M., Osmałek, T. Z., & Waszyk-Nowaczyk, M. (2024) · Medical Science Monitor · doi:10.12659/msm.944605 · Cited by 28 · see citing works
“By analyzing large datasets, machine learning is helping to optimize treatment and adherence, particularly for complex diseases [56].”
BackgroundRevolutionizing nursing education and care: The role of artificial intelligence in nursing
Ghane, G., Ghiyasvandian, S., Chekeni, A., & Karimi, R. (2024) · Nurse Author & Editor · doi:10.1111/nae2.12057 · Cited by 19 · see citing works
Kapse, S., Yenurkar, G., Nyangaresi, V., Balpande, G., Kale, S., Jadhav, M., Lawankar, S., & Jaunjale, V. (2025) · Comput. Mater. Continua · doi:10.32604/cmc.2025.061981 · Cited by 19
“Similarly, a study by Frontiers in Digital Health [44] explored the application of artificial intelligence (AI) in enhancing medication adherence among elderly patients.”
Aryffin, H. A. K., Sahbudin, M. A. B., Pitchay, S. A., Halim, A. H. A., & Sahbudin, I. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2874 · Cited by 18 · see citing works
Hoelscher, S. H., Taylor-Pearson, K., & Wei, H. (2024) · Nurse Leader · doi:10.1016/j.mnl.2024.07.011 · Cited by 26 · see citing works
Mottaghi-Dastjerdi, N., & Soltany-Rezaee-Rad, M. (2024) · Iranian journal of pharmaceutical research · doi:10.5812/ijpr-150510 · Cited by 17 · see citing works
“Applications and devices powered by AI can track patient adherence to medication regimens, send reminders, and notify healthcare providers if patients are not following prescribed therapies (55).”
Simpson, M. D., & Qasim, H. S. (2025) · Pharmacy · doi:10.3390/pharmacy13020041 · Cited by 26 · see citing works
“In an effort to devise personalised treatment regimens for mental illness patients, researchers at the University of California, Los Angeles (UCLA) have used artificial intelligence to analyse genomic data [63].”
Kelkar, A. H., Hantel, A., Koranteng, E., Cutler, C., Hammer, M. J., & Abel, G. A. (2023) · JCO Oncology Practice · doi:10.1200/op.23.00412 · Cited by 16 · see citing works
Eiselt, A.-K., Kirkendall, S., Xiong, E., Langner, D., & Goldfarb, M. B. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1713334 · Cited by 17 · see citing works
The role of artificial intelligence in advancing urologic care: From diagnostics to therapeutics
Samenezhad, S., & Rafighi, D. (2025) · Surgery in Practice and Science · doi:10.1016/j.sipas.2025.100322 · Cited by 15 · see citing works
“Additional research must justify these tools among diverse patient groups and in varied clinical settings, especially in view of pandemic-facilitated accelerated implementation of digital health technologies to counteract COVID-19 [44].”
Saigal, K., Patel, A., & Lucke-Wold, B. (2023) · Medicina · doi:10.3390/medicina59101714 · Cited by 15
Isola, S., Murdaca, G., Brunetto, S., Zumbo, E., Tonacci, A., & Gangemi, S. (2024) · Informatics · doi:10.3390/informatics11040086 · Cited by 21 · see citing works
“…health data, there is a need for more advanced statistical approaches focusing on complex mixtures of exposures; therefore, the management of chronic diseases is a task perfectly suited to innovative technologies such as AI that can speed up the development of personalized treatments [17].”
Healthcare provider interventions to support medication adherence: state-of-the-science overview
Crawshaw, J., & McCleary, N. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1567967 · Cited by 15 · see citing works
Zavaleta-Monestel, E., Bogantes, L. C. M., Chavarría-Rodríguez, S., Arguedas-Chácon, S., Bastos-Soto, N., & Villalobos-Madriz, J. (2025) · Cureus · doi:10.7759/cureus.83132 · Cited by 12 · see citing works
“AI-based tools offer a promising avenue to address these complexities by enhancing patient monitoring, predicting adherence risks, personalizing interventions, and facilitating communication between healthcare providers and patients [6].”
Artificial Intelligence in Healthcare: Current Trends and Future Directions
Samajdar, S. S., Chatterjee, R., Mukherjee, S., Dey, A., Saboo, B., Pal, J., Joshi, S., & Chatterjee, N. (2025) · Current Medical Issues · doi:10.4103/cmi.cmi_93_24 · Cited by 16 · see citing works
Artificial intelligence in health care: A review of uses, challenges and potential uses
Makade, J. G., Bankar, N. J., Kumar, A., Bandre, G. R., & Yenurkar, G. K. (2024) · AIP conference proceedings · doi:10.1063/5.0240230 · Cited by 12 · see citing works
Kassem, R. G., Mbata, A. O., Usuemerai, P. A., Abass, L., & Ogbewele, E. G. (2020) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.15.2.0792 · Cited by 11
“AI tools helped the pharmacy identify emerging health trends, such as a growing demand for mental health medications and treatments for chronic conditions like diabetes and hypertension (Babel et al ., 2021).”
BackgroundResponsible scaling of artificial intelligence in healthcare: standardization meets customization
Lukkien, D., Nap, H. H., Peine, A., Minkman, M. M. N., Moors, E. H. M., & Boon, W. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09842-5 · Cited by 8 · see citing works
Management of polypharmacy through deprescribing in older patients: a review of the role of AI tools
Meslamani, A. Z. A. (2025) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2025.2519648 · Cited by 10 · see citing works
“AI tools have been used to improve medication adherence [24], medication reconciliation [25], and personalized treatment strategies [26].”
Digital health for allergen immunotherapy
Dramburg, S., Matricardi, P. M., Pfaar, O., & Klimek, L. (2022) · Allergologie select · doi:10.5414/alx02301e · Cited by 13 · see citing works
Harnessing machine learning for improved diagnosis, drug discovery, and patient care
Paul, J. K., Azmal, M., Talukder, O. F., Haque, A. S. N. B., Meem, M., & Ghosh, A. (2025) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2025.100051 · Cited by 8 · see citing works
Hisan, U. K., Widjanarko, B., Sriatmi, A., & Shaluhiyah, Z. (2025) · Korean Journal of Family Medicine · doi:10.4082/kjfm.25.0018 · Cited by 10 · see citing works
“These tools can also capture data on mood, sleep, or physical activity, which may reveal patterns that help tailor individual support plans [32,33].”
The good, the bad, and the binary
Hoelscher, S. H. (2025) · Nursing · doi:10.1097/nsg.0000000000000160 · Cited by 12 · see citing works
Zeng, L., Li, Q.-Q., Zuo, Y., Zhang, Y., & Li, Z. (2025) · Journal of Medical Internet Research · doi:10.2196/71418 · Cited by 8 · see citing works
Ghozali, M. (2024) · 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac61858.2024.10714843 · Cited by 8 · see citing works
“The advancements in the technology, notably in machine learning (ML) and artificial intelligence (AI), offer promising solutions for predicting and improving a patient’s adherence [2].”
BackgroundPetković, K., Strika, Z., Likić, R., & Lucijanić, M. (2024) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.16241 · Cited by 8 · see citing works
“For instance, AI solutions can increase medication adherence in patients with noncom-municable diseases by organizing and mining data from unstructured electronic medical records as reported by Babel et al. (2021).”
Could Artificial Intelligence be a Therapeutic for Mental Issues?
Darzi, P. (2023) · Science Insights · doi:10.15354/si.23.co132 · Cited by 12 · see citing works
“AI-driven applications, such as those powered by AI, have the capability to provide users with re-minders to engage in self-care activities, employ relaxation techniques, or adhere to prescription schedules (3).”
BackgroundHou, J., Zhang, Z., Cheng, X., & Wang, W. (2026) · Journal of Medical Internet Research · doi:10.2196/85663 · Cited by 8 · see citing works
“For instance, patients worry that AI cannot account for their individualized health variations—such as differences in underlying conditions or lifestyle hab-its—potentially compromising the effectiveness of medical interventions [58].”
Ethical AI innovation in healthcare and sustainable development in Bangladesh
Hasan, K. K., Akhtar, M. M., Hasnat, M. A., & Khandakar, H. (2026) · Journal of Health Organization and Management · doi:10.1108/jhom-12-2025-0862 · Cited by 7 · see citing works
Juyal, A., Bisht, S., & Singh, M. F. (2024) · Blood Pressure Monitoring · doi:10.1097/mbp.0000000000000711 · Cited by 12 · see citing works
“Conversational AI in chronic disease patients showed engagement with AI and its impact on improving refill adherence was influenced by factors like race, ethnicity, language, age, and social determinants [38].”
Artificial intelligence-assisted nursing care: a concept analysis using Walker and Avant approach
Maleki, R. N., Shahbazi, S., Hoseinzadeh, M., Ghafourifard, M., Gougjehyaran, H. G., & Faravan, A. (2025) · BMC Nursing · doi:10.1186/s12912-025-03818-y · Cited by 12 · see citing works
“In addition, AI systems increasingly support medication management, including medication reconciliation, drug interaction checks, and personalized dosing, hence contributing to decreased medication errors and improvement in patient safety [8].”
Digital health tools in the management of allergic rhinitis: a systematic review
Alotaibi, A. D., Alqahtani, A. S., Alateeq, M., Saleem, M., & Khan, M. S. (2025) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-025-09526-2 · Cited by 6 · see citing works
Stakeholders' Perspectives on Medication Adherence Enhancing Interventions.
Borah, B., Pieretti, L. J., Balch, A. J., Wani, R., Daly, C. J., Dawoud, D., Hutchins, D., Hiligsmann, M., Peterson, A. M., & Ágh, T. (2025) · Value in Health · doi:10.1016/j.jval.2025.01.022 · Cited by 6 · see citing works
Li, X.-L., Yue, X., Zhang, L., Zheng, X.-J., & Shang, N. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1534552 · Cited by 6 · see citing works
Artificial intelligence-driven drug interaction prediction
Sonaji, P., Subramanian, L., & Rajesh, M. (2024) · World Journal of Biology Pharmacy and Health Sciences · doi:10.30574/wjbphs.2024.17.2.0070 · Cited by 6
Designing an AI Chatbot for Team-Based Diabetes Care: An Iterative Human-in-the-Loop Approach
Tseng, Y.-C., Chen, S., Mah, K.-H., & Chen, Y.-C. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-93736-1_17 · Cited by 5 · see citing works
Choudhary, N., Kumar, D., Jyoti, T. P., Prajapati, B. G., Kchaou, M., Webster, T. J., & Faiyazuddin, M. (2026) · International Journal of Nanomedicine · doi:10.2147/ijn.s575214 · Cited by 5 · see citing works
Luengo-Polo, J., Caballero, D. C., Jiménez, B. R., & Juárez, L. M. (2025) · BMC Nursing · doi:10.1186/s12912-025-03964-3 · Cited by 4 · see citing works
“With the growing prevalence of smartphones [23] and the expanding potential of Artificial Intelligence (AI) technologies [24, 25], the use of virtual assistants to support tasks such as medication reminders and medical appointment monitoring has become increasingly common [26].”
Christopher, C., Pathak, N., Babar, Z.-U.-D., & Shrestha, S. (2025) · Aging Medicine · doi:10.1002/agm2.70051 · Cited by 5 · see citing works
“AI-driven interventions have demonstrated the capacity to enhance medication safety, adherence, and polypharmacy reduction, yet their application remains fragmented [42, 62].”
Pokorny, M. A., Liang, V., Thorne, P. R., MacFater, W., Habib, A., Mahadevan, M., & Holt, E. (2025) · BMC Digital Health · doi:10.1186/s44247-025-00202-3 · Cited by 5 · see citing works
“Additionally, in other healthcare domains, such as medication adherence for non-communicable diseases, there is preliminary evidence that AI tools may provide superior benefits compared to traditional healthcare delivery [26].”
Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication
Marineci, C., Văleanu, A., Chiriță, C., Negreș, S., Stoicescu, C., & Chioncel, V. (2025) · Medicina · doi:10.3390/medicina61071313 · Cited by 5 · see citing works
“In this way, ML-driven technologies play a critical role in enhancing the quality of the healthcare delivery and mitigating the negative consequences of non-adherence on both patient outcomes and healthcare system costs [11].”
Artificial Intelligence: Future Aspects in the Pharmaceutical Industry an Overview
Bairagi, A., Singhai, A., & Jain, A. (2024) · Asian Journal of Pharmacy and Technology · doi:10.52711/2231-5713.2024.00039 · Cited by 5 · see citing works
A GPT-reinforced social robot for patient communication: a pilot study
Klooster, J. V. V. '., Capasso, M., van Gorssel, D., Vrolijk, E., Rettagliata, G., Gerritsen, D., Hegeman, M. C., Tauro, E., Caiani, E., & Vonkeman, H. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1653168 · Cited by 4 · see citing works
Click, care, connect: incorporating nursing informatics into undergraduate nursing curricula
Harerimana, A., Wicking, K., Biedermann, N., & Yates, K. (2025) · Informatics for Health and Social Care · doi:10.1080/17538157.2025.2582852 · Cited by 3 · see citing works
Arısan, A., Genç, C., & Duran, G. S. (2025) · Bioengineering · doi:10.3390/bioengineering12111165 · Cited by 4 · see citing works
“To enhance engagement and adherence, AI-based approaches have also been investigated as promising tools for addressing behavioral challenges such as compliance and communication in healthcare [16].”
A review on recent advancements of ChatGPT and datafication in healthcare applications
Jagatheesaperumal, S. K., Pandiyarajan, A., Boopathy, P., Deepa, N., Barreto, A. G., & de Albuquerque, V. D. (2025) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2025.110885 · Cited by 4 · see citing works
Potential role of large language models and personalized medicine to innovate cardiac rehabilitation
Mishra, R., Patel, H., Jamal, A., & Singh, S. (2025) · World Journal of Clinical Cases · doi:10.12998/wjcc.v13.i19.98095 · Cited by 4 · see citing works
Harnessing the potential of ChatGPT in pharmacy management: a concise review
Noman, A. A., Fahim, M. I. A., Tonny, T. S., Samia, A. A., & Moinuddin, S. M. (2024) · Exploration of Digital Health Technologies · doi:10.37349/edht.2024.00026 · Cited by 5 · see citing works
Zhao, S., Zhao, L., Xu, X., & You, H. (2023) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580231214469 · Cited by 3 · see citing works
“If necessary, artificial intelligence, remote monitoring and other technologies can be applied to enhance the capacity for drug management; 63,64 3) Social support: the assistance and guidance of family members, peers and medical staff has a positive effect on patients’ long-term self-care,…”
BackgroundArtificial intelligence in the management of hypertension: a narrative review
Zhou, J., Sun, M., Guan, Z., Shen, J., Chen, T., Zeng, D., Zhu, J., & Li, H. (2025) · The Visual Computer · doi:10.1007/s00371-025-04045-x · Cited by 3 · see citing works
Fahmy, A. (2025) · Premier Journal of Public Health · doi:10.70389/pjph.100021 · Cited by 3 · see citing works
Enhancing Shared Decision-Making in Cardiology with Artificial Intelligence
Çiçek, V., & Bağcı, U. (2025) · Balkan Medical Journal · doi:10.4274/balkanmedj.galenos.2025.2025-1-133 · Cited by 1 · see citing works
“Babel et al. 10 evaluated an AI-powered smartphone application designed to track medication intake in stroke patients.”
Koh, Y. L., Chua, K. Y. K., Ding, X.-N., Aau, W., & Tan, N. C. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1516062 · Cited by 3 · see citing works
ARSLANTAŞ, S. (2024) · Journal of Health Organization and Management · doi:10.1108/jhom-06-2024-0241 · Cited by 4 · see citing works
Using AI to increase medication adherence
Dave, P. (2024) · Asian Journal of Dental and Health Sciences · doi:10.22270/ajdhs.v4i2.80 · Cited by 3 · see citing works
Integration of artificial intelligence applications in clinical pharmacy services: A scoping review
Al‐Taie, A., Abdullah, A. S., & Arueyingho, O. (2026) · Future Healthcare Journal · doi:10.1016/j.fhj.2026.100547 · Cited by 1 · see citing works
Sallam, M., Stanley, A., Snygg, J., Al-Shakerchi, H., Atragchi, O. A., Abusamra, R., & Sallam, M. (2026) · Recent Progress in Sciences · doi:10.70462/rps.2026.3.001 · Cited by 2 · see citing works
“The appeal of genAI applications in patient self-management lies in its immediacy, convenience, and adapt-ability [40-42].”
Kalita, B., Singh, K. A., & Kalita, M. (2026) · Indian Journal of Community Medicine · doi:10.4103/ijcm.ijcm_454_24 · Cited by 2
“[14,15] Various types of machine learning-based predictions are available, such as Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Stochastic Gradient Descent (SGD), Extra Trees Classifier, Multilayer Perceptron (MLP), and…”
Patient Voice and Treatment Nonadherence in Cancer Care: A Scoping Review of Sentiment Analysis
Wreyford, L., Gururajan, R., Zhou, X., & Higgins, N. (2026) · Nursing Reports · doi:10.3390/nursrep16010018 · Cited by 2 · see citing works
“To improve readability, we consolidate key insights across subsections below, focusing on evidential strength (measured vs. inferred), platform differences (social vs. EHR), and practical implications for nursing.”
- Authors
- Babel, A., Taneja, R., Mondello Malvestiti, F., Monaco, A., & Donde, S.
- Affiliations
- Leeds Teaching Hospitals NHS Trust (GB)
- Pfizer (India) (IN)
- HEC Paris (FR)
- Viatris (US)
- Corresponding author
- Babel, Aditi
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2021-06-29
- Volume
- 3
- Pages
- 669869-669869
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC8521858
- OpenAlex
- W3176812453
- Cited by
- 199
- References
- 72
- Citations by year
- 2026: 40 · 2025: 69 · 2024: 47 · 2023: 37 · 2022: 6
- FWCI
- 5.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- artificial intelligence, medication adherence, non-communicable diseases, mobile health apps, medication reminder system, patient empowerment, integrated care, machine learning, digital health technologies, patient-physician communication, health care disparities, COVID-19 pandemic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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