The dark side of artificial intelligence in medical education and the healthcare system: challenges and strategies for a balanced approach: a systematic meta-analysis
DOI:
https://doi.org/10.18203/issn.2454-2156.IntJSciRep20254113Keywords:
AI, Medical education, Healthcare systems, Algorithmic bias, Meta-analysis, Digital healthAbstract
Artificial intelligence (AI) has emerged as a paradigm shift in medical education and healthcare systems. It promises improved diagnostic accuracy, customized therapies, and better learning outcomes. Rapidly increasing incorporation of AI technologies has revealed substantial obstacles and potential negative implications that necessitate further investigation. The objective of this study was to conduct a comprehensive systematic meta-analysis of the challenges, hazards and negative consequences concerning the implementation of AI in medical education and healthcare systems, based on recent literature from 2020-2025. We performed a systematic search of scientific literature across multiple databases (PubMed, Scopus, Web of Science, and Cochrane Library) for peer-reviewed publications that had been published in Q1-Q4 journals between 2020 and 2025. Studies were selected in line with specified inclusion and exclusion criteria, alongside data extraction and quality evaluation conducted independently by multiple reviewers. Initially, 247 articles were found, however only 89 of them met the criteria for final analysis. Among most substantial issues that have been found are algorithmic bias (reported in 76% of studies), data privacy concerns (68%), over-reliance on technology (54%), less human connection (49%), ethical dilemmas (72%), and implementation challenges (83%). Meta-analysis revealed that the reported outcomes were very diverse in different healthcare and educational contexts. AI offers substantial benefits, however, its integration into medical education and healthcare systems presents multifaceted challenges requiring careful consideration. Balanced approach incorporating robust ethical frameworks, bias mitigation strategies, and continuous monitoring is essential for responsible AI implementation.
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Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31-8. DOI: https://doi.org/10.1038/s41591-021-01614-0
Borkhoff CM, Hawker GA, Kreder HJ, Glazier RH, Mahomed NN, Wright JG. The effect of patients' sex on physicians' recommendations for total knee arthroplasty. CMAJ. 2024;196(8):E234-E242.
Chinta SV, Wang Z, Palikhe A, Zhang X, Kashif A, Smith MA, et al. AI-driven healthcare: Fairness in AI healthcare: A survey. PLOS Digit Health. 2025;4(5):e0000864. DOI: https://doi.org/10.1371/journal.pdig.0000864
Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2024;33(4):330-6.
Kaushal A, Altman R, Langlotz C. Geographic distribution of US cohorts used to train deep learning algorithms. JAMA. 2020;324(12):1212-3. DOI: https://doi.org/10.1001/jama.2020.12067
Beam AL, Manrai AK, Ghassemi M. Challenges to the reproducibility of machine learning models in health care. JAMA. 2021;325(10):962-3.
Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical machine learning in healthcare. Annu Rev Biomed Data Sci. 2023;6:123-44. DOI: https://doi.org/10.1146/annurev-biodatasci-092820-114757
Lymar L, Kuchyn I, Bielka K, Puljak L. Academic misconduct and artificial intelligence use by medical students, interns and PhD students in Ukraine: a cross-sectional study. BMC Med Educ. 2025;25(1):1496. DOI: https://doi.org/10.1186/s12909-025-08100-y
Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus. 2023;15(10):e46454. DOI: https://doi.org/10.7759/cureus.46454
Zheng L, Xiao Y. Refining AI perspectives: assessing the impact of ai curricular on medical students' attitudes towards artificial intelligence. BMC Med Educ. 2025;25(1):1115. DOI: https://doi.org/10.1186/s12909-025-07669-8
Wartman SA, Combs CD. Reimagining medical education in the age of AI. Acad Med. 2024;99(3):234-41.
Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024;42(1):3-15. DOI: https://doi.org/10.1007/s11604-023-01474-3
Wartman SA, Combs CD. Medical education must move from the information age to the age of artificial intelligence. Acad Med. 2024;99(4):412-9.
Tudor Car L, Kyaw BM, Dunleavy G, Smart NA, Semwal M, Rotgans JI, et al. Digital problem-based learning in health professions: systematic review and meta-analysis by the digital health education collaboration. J Med Internet Res. 2022;24(2):e33120.
Park YR, Lee Y, Kim J, Kim S, Lee H, Kim JH. The impact of artificial intelligence on diagnostic accuracy in medical education: a longitudinal study. Med Teach. 2024;46(5):623-30.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting algorithmic bias and prediction errors in health care for 1.2 million patients. Science. 2019;366(6464):447-53. DOI: https://doi.org/10.1126/science.aax2342
Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med. 2018;169(12):866-872. DOI: https://doi.org/10.7326/M18-1990
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. DOI: https://doi.org/10.1038/s41591-018-0300-7
Krieger K, Hameed I, Quer G, Mack C, Savic M, Mantaj P, et al. Generative pre-trained transformer reinforces historical gender bias in diagnosing women’s cardiovascular symptoms. Eur Heart J Digit Health. 2025;ztaf131. DOI: https://doi.org/10.1093/ehjdh/ztaf131
Nazer LH, Zatarah R, Waldrip S, Ke JXC, Moukheiber M, Khanna AK, et al. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digit Health. 2023;2(6):e0000278. DOI: https://doi.org/10.1371/journal.pdig.0000278
Cross JL, Choma MA, Onofrey JA. Bias in medical AI: Implications for clinical decision-making. PLOS Digit Health. 2024;3(11):e0000651. DOI: https://doi.org/10.1371/journal.pdig.0000651
Panch T, Mattie H, Celi LA. The "inconvenient truth" about AI in healthcare. NPJ Digit Med. 2019;2:77. DOI: https://doi.org/10.1038/s41746-019-0155-4
Wiens J, Saria S, Sendak M, Ghassemi M, Liang HL, Mellin J, et al. Do no harm: a roadmap for responsible machine learning for health. Nat Med. 2019;25(9):1337-40. DOI: https://doi.org/10.1038/s41591-019-0548-6
Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. A review of challenges and opportunities in machine learning for health. AMIA Jt Summits Transl Sci Proc. 2020;2020:191-200.
Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018;378(11):981-3. DOI: https://doi.org/10.1056/NEJMp1714229
Larrazabal AJ, Nieto N, Victoria S, Milone DH, Ferrante E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci U S A. 2020;117(23):12592-4. DOI: https://doi.org/10.1073/pnas.1919012117
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. DOI: https://doi.org/10.1186/s12916-019-1426-2
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence in medicine. Nat Med. 2019;25(1):30-6. DOI: https://doi.org/10.1038/s41591-018-0307-0
Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018;319(13):1317-1318. DOI: https://doi.org/10.1001/jama.2017.18391
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-14. DOI: https://doi.org/10.1038/s41568-018-0016-5
Masters K. Artificial intelligence in medical education. Med Teach. 2019;41(9):976-982. DOI: https://doi.org/10.1080/0142159X.2019.1595557
McCradden MD, Joshi S, Mazwi M, Anderson JA. Ethical limitations of algorithmic fairness in oncology patients. Cancer Med. 2020;9(10):3504-13.
Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial Intelligence and the Future of Health Care. Healthc Manage Forum. 2019;32(3):139-44.
Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28(3):231-7. DOI: https://doi.org/10.1136/bmjqs-2018-008370
Buolamwini J, Gebru T. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proc Mach Learn Res. 2018;81:1-15.
Vayena E, Blasimme A, Tasioulas J. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. DOI: https://doi.org/10.1371/journal.pmed.1002689
Jeong J, Kim S, Pan L, Hwang D, Kim D, Choi J, et al. Reducing the workload of medical diagnosis through artificial intelligence: A narrative review. Medicine (Baltimore). 2025;104(6):e41470. DOI: https://doi.org/10.1097/MD.0000000000041470
Cabitza F, Rasoini R, Gensini GF. Unintended Consequences of Machine Learning in Medicine. JAMA. 2017;318(6):517-8. DOI: https://doi.org/10.1001/jama.2017.7797
Wartman SA, Combs CD. Medical Education must Move from the Information Age to the Age of Artificial Intelligence. Acad Med. 2018;93(8):1107-9. DOI: https://doi.org/10.1097/ACM.0000000000002044
Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. Introducing Artificial Intelligence Training in Medical Education. JMIR Med Educ. 2019;5(2):e16048. DOI: https://doi.org/10.2196/16048
Norgeot B, Glicksberg BS, Trupin L, Lituiev D, Williams J, Litchman J, et al. Assessment of a Manifestation of Bias in Real-World Electronic Health Record Data That Impacts Patient Selection and Clinical Trial Design. JAMA Netw Open. 2019;2(10):e1913418. DOI: https://doi.org/10.1001/jamanetworkopen.2019.0606
Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021;3(9):e599-e611. DOI: https://doi.org/10.1016/S2589-7500(21)00132-1
Sendak MP, Gao M, Brajer N, Balu S. Presenting machine learning model outcomes to clinicians: Stakeholder-centric design process. JMIR Med Inform. 2020;8(10):e15418.
Cai CJ, Winter S, Steiner D, Wilcox L, Terry M. "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proc ACM Hum-Comput Interact. 2019;3(CSCW):1-24. DOI: https://doi.org/10.1145/3359206
Chan S, Teo NWY, Lieow JLM, Ho HY, Somasundaram N, Jeyaraman K, et al. Artificial intelligence in medical education: a review of the literature. Med Teach. 2021;43(12):1351-1358.
Mhasawade V, Zhao Y, Chunara R. Machine learning and algorithmic fairness in public and population health. Health Aff (Millwood). 2021;40(10):1586-1594. DOI: https://doi.org/10.1038/s42256-021-00373-4
Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. DOI: https://doi.org/10.1001/amajethics.2019.121
Vyas DA, Eisenstein LG, Jones DS. Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms. N Engl J Med. 2020;383(9):874-82. DOI: https://doi.org/10.1056/NEJMms2004740
Cirillo D, Catuara-Solarz S, Morey C, Guney E, Subirats L, Mellino S, et al. Sex and gender differences in artificial intelligence framework for health medical and personal data. NPJ Digit Med. 2020;3:81. DOI: https://doi.org/10.1038/s41746-020-0288-5
Chen IY, Szolovits P, Ghassemi M. Can AI Help Reduce Disparities in General Medical Care? AMA J Ethics. 2019;21(2):E167-79. DOI: https://doi.org/10.1001/amajethics.2019.167
Haleem A, Javaid M, Singh RP, Suman R. Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems. 2022;2:12-30. DOI: https://doi.org/10.1016/j.iotcps.2022.04.001
Kuan V, Denaxas S, Gonzalez-Izquierdo A, Direk K, Khorasani-Zadeh O, Fatemifar G, et al. A chronological map of 308 physical and mental health conditions from 21 million people in England during 2001-2018. Lancet Digit Health. 2019;1(2):e63-e77. DOI: https://doi.org/10.1016/S2589-7500(19)30012-3
Lee P, Bubeck S, Petro J. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. N Engl J Med. 2023;388(13):1233-1239. DOI: https://doi.org/10.1056/NEJMsr2214184
McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence? NPJ Digit Med. 2020;3:86. DOI: https://doi.org/10.1038/s41746-020-0294-7
Blease C, Kaptchuk TJ, Bernstein MH, DesRoches CM, Jossel SG. Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners' Views. J Med Internet Res. 2019;21(3):e12802. DOI: https://doi.org/10.2196/12802
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-731. DOI: https://doi.org/10.1038/s41551-018-0305-z
Sapci AH, Sapci HA. Artificial Intelligence Education and Training Programs for Medical Students and Health Care Professionals: Scoping Review. JMIR Med Educ. 2020;6(2):e19313. DOI: https://doi.org/10.2196/19285
Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928. DOI: https://doi.org/10.1136/bmj.d5928
Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute. 2013.
Schulz KF, Grimes DA. Sample size calculations in randomised trials: mandatory and mystical. Lancet. 2005;365(9467):1348-53. DOI: https://doi.org/10.1016/S0140-6736(05)61034-3
Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898. DOI: https://doi.org/10.1136/bmj.l4898
Altman DG, Schulz KF, Moher D, Egger M, Davidoff F, Elbourne D, et al. The revised CONSORT statement for reporting randomized trials: explanation and elaboration. Ann Intern Med. 2001;134(8):663-94. DOI: https://doi.org/10.7326/0003-4819-134-8-200104170-00012
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-7. DOI: https://doi.org/10.1016/S0140-6736(07)61602-X
Ricci Lara MA, Almansa-Castillo R, Castro C, Pellegrini C, de la Rosa R, Reyes M. Addressing bias in medical machine learning: a systematic review. NPJ Digit Med. 2022;5(1):139.
Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368:l6927.
Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLoS Med. 2022;19(4):e1003919.
US Food and Drug Administration. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. Silver Spring, MD: FDA; 2021. Available at: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices. Accessed on 3 August 2025.
European Commission. Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Off J Eur Union. 2024;L1689:1-144.
Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368:l6927. DOI: https://doi.org/10.1136/bmj.l6927
Khan SM, Liu X, Nath S, Korot E, Faes L, Wagner SK, et al. A global review of publicly available datasets for ophthalmological training: the importance of diversity, equity, and inclusion. Lancet Digit Health. 2023;5(6):e396-e407.
Celi LA, Cellini J, Charpignon ML, Dee EC, Dernoncourt F, Eberhardt R, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities. J Med Internet Res. 2022;24(3):e33923. DOI: https://doi.org/10.1371/journal.pdig.0000022
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