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Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education

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Article: 2330250 | Received 30 Jan 2024, Accepted 08 Mar 2024, Published online: 03 Apr 2024

References

  • Masters K. Artificial intelligence in medical education. Med Teach. 2019;41(9):976–8. doi: 10.1080/0142159X.2019.1595557
  • Lee J, Wu AS, Li D, et al. Artificial intelligence in undergraduate medical education: a scoping review. Acad Med. 2021;96(11S):S62–S70. doi: 10.1097/ACM.0000000000004291
  • Tolsgaard MG, Pusic MV, Sebok-Syer SS, et al. The fundamentals of artificial intelligence in medical education research: AMEE guide No. 156. Med Teach. 2023;45(6):565–573. doi: 10.1080/0142159X.2023.2180340
  • Nagi F, Salih R, Alzubaidi M, et al. Applications of artificial intelligence (AI) in medical education: a scoping review. Stud Health Technol Inform. 2023;305:648–651. doi: 10.3233/SHTI230581
  • Grunhut J, Marques O, Wyatt ATM. Needs, challenges, and applications of artificial intelligence in medical education curriculum. JMIR Med Educ. 2022;8(2):e35587. doi: 10.2196/35587
  • United Nations. The 2030 agenda and the sustainable development goals: an opportunity for Latin America and the Caribbean. Santiago, Chile: United Nations Publication; 2018. pp. LC/G.2681–P/Rev.3.
  • Wang W, Wang G, Marivate V, et al. On the transparency of large AI models. Patterns (New York NY). 2023;4(7):100797. doi: 10.1016/j.patter.2023.100797
  • He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nature Med. 2019;25(1):30–36. doi: 10.1038/s41591-018-0307-0
  • Haibe-Kains B, Adam GA, Hosny A, et al. Massive Analysis Quality Control (MAQC) society board of directors, Waldron, L et al (2020). Transparency and reproducibility in artificial intelligence. Nature. 586(7829):E14–E16. doi: 10.1038/s41586-020-2766-y
  • Daneshjou R, Smith MP, Sun MD, et al. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review. JAMA Dermatol. 2021;157(11):1362–1369. doi: 10.1001/jamadermatol.2021.3129
  • Gurupur V, Wan TTH. Inherent bias in artificial intelligence-based decision support systems for healthcare. Medicina (Kaunas). 2020;56(3):141. doi: 10.3390/medicina56030141
  • Nelson GS. Bias in Artificial Intelligence. N C Med J. 2019;80(4):220–222. doi: 10.18043/ncm.80.4.220
  • Tran Z, Byun J, Lee HY, et al. Bias in artificial intelligence in vascular surgery. Semin Vasc Surg. 2023;36(3):430–434. doi: 10.1053/j.semvascsurg.2023.07.003
  • Savage TR. Artificial Intelligence in Medical Education. Acad Med. 2021;96(9):1229–1230. doi: 10.1097/ACM.0000000000004183
  • Vazquez-Zapien GJ, Mata-Miranda MM, Garibay-Gonzalez F, et al. Artificial intelligence model validation before its application in clinical diagnosis assistance. World J Gastroenterol. 2022;28(5):602–604. doi: 10.3748/wjg.v28.i5.602
  • Boscardin CK, Gin B, Golde PB, et al. ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity. Acad Med. 2024;99(1):22–27. doi: 10.1097/ACM.0000000000005439
  • Lang J, Repp H. Artificial intelligence in medical education and the meaning of interaction with natural intelligence - an interdisciplinary approach. GMS J Med Educ. 2020;37(6):Doc59. doi: 10.3205/zma001352
  • Jowsey T, Stokes-Parish J, Singleton R, et al. Medical education empowered by generative artificial intelligence large language models. Trends Mol Med. 2023;29(12):971–973. doi: 10.1016/j.molmed.2023.08.012
  • Khalid N, Qayyum A, Bilal M, et al. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med. 2023;158:106848. doi: 10.1016/j.compbiomed.2023.106848
  • Goldsteen A, Farkash A, Moffie M, et al. Applying artificial intelligence privacy technology in the healthcare domain. Stud Health Technol Inform. 2022;294:121–122. doi: 10.3233/SHTI220410
  • 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
  • Car J, Sheikh A, Wicks P, et al. Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom. BMC Med. 2019;17(1):143. doi: 10.1186/s12916-019-1382-x
  • Shehab M, Abualigah L, Shambour Q, et al. Machine learning in medical applications: a review of state-of-the-art methods. Comput Biol Med. 2022;145:105458. doi: 10.1016/j.compbiomed.2022.105458
  • Office for Civil Rights, H. H. S. Standards for privacy of individually identifiable health information. Final Rule Federal Register. 2002:67(157):53181–53273.
  • Taber C, Warren J, Day K. Improving the quality of informed consent in clinical research with information technology. Stud Health Technol Inform. 2016;231:135–142.
  • Amann J, Blasimme A, Vayena E, et al. & Precise4Q consortium (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310. doi: 10.1186/s12911-020-01332-6
  • Rodgers CM, Ellingson SR, Chatterjee P. Open Data and transparency in artificial intelligence and machine learning: a new era of research. F1000Res. 2023;12:387. doi: 10.12688/f1000research.133019.1
  • Ng FYC, Thirunavukarasu AJ, Cheng H, et al. Artificial intelligence education: an evidence-based medicine approach for consumers, translators, and developers. Cell Reports Medicine. 2023;4(10):101230.
  • Pupic N, Ghaffari-Zadeh A, Hu R, et al. An evidence-based approach to artificial intelligence education for medical students: a systematic review. PLOS Digital Health. 2023;2(11):e0000255. doi: 10.1371/journal.pdig.0000255
  • Yang L, Ene IC, Arabi Belaghi R, et al. Stakeholders’ perspectives on the future of artificial intelligence in radiology: a scoping review. Eur Radiol. 2022;32(3):1477–1495. doi: 10.1007/s00330-021-08214-z
  • Abhee SS, Phillips R. How artificial intelligence (AI) could have helped our medical education during the COVID-19 pandemic - a student’s perspective. Med Teach. 2020;42(11):1315–1316. doi: 10.1080/0142159X.2020.1798371
  • Li Q, Qin Y. AI in medical education: medical student perception, curriculum recommendations and design suggestions. BMC Med Educ. 2023;23(1):852. doi: 10.1186/s12909-023-04700-8
  • Azer SA, Guerrero APS. The challenges imposed by artificial intelligence: are we ready in medical education? BMC Med Educ. 2023;23(1):680. doi: 10.1186/s12909-023-04660-z
  • Russell RG, Lovett Novak L, Patel M, et al. Competencies for the use of artificial intelligence-based tools by health care professionals. Acad Med. 2023;98(3):348–356. doi: 10.1097/ACM.0000000000004963
  • O’Connor S. Teaching artificial intelligence to nursing and midwifery students. Nurse Educ Pract. 2022;64:103451. doi: 10.1016/j.nepr.2022.103451
  • Çalışkan SA, Demir K, Karaca O, et al. Artificial intelligence in medical education curriculum: An e-Delphi study for competencies. PLoS One. 2022;17(7):e0271872. doi: 10.1371/journal.pone.0271872
  • Hathaway QA, Hogg JP, Lakhani DA. Need for medical Student education in emerging technologies and artificial intelligence: fostering enthusiasm, rather than flight, from specialties most affected by emerging technologies. Acad Radiol. 2023;30(8):1770–1771. doi: 10.1016/j.acra.2022.11.018
  • Park SH, Do KH, Kim S, et al. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019;16:18. doi: 10.3352/jeehp.2019.16.18
  • Pucchio A, Rathagirishnan R, Caton N, et al. Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study. BMC Med Educ. 2022;22(1):815. doi: 10.1186/s12909-022-03896-5
  • Fischetti C, Bhatter P, Frisch E, et al. The evolving importance of artificial intelligence and radiology in medical trainee education. Acad Radiol. 2022;29 Suppl 5:S70–S75. doi: 10.1016/j.acra.2021.03.023
  • Kalpathy-Cramer J, Patel JB, Bridge C, et al. Basic Artificial Intelligence Techniques: Evaluation of Artificial Intelligence Performance. Radiol Clin North Am. 2021;59(6):941–954. doi: 10.1016/j.rcl.2021.06.005
  • Knopp MI, Warm EJ, Weber D, et al. AI-Enabled medical education: threads of change, promising futures, and risky realities across four potential future worlds. JMIR Med Educ. 2023;9:e50373. doi: 10.2196/50373
  • Larson DB, Harvey H, Rubin DL, et al. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J Am Coll Radiol. 2021;18(3 Pt A):413–424. doi: 10.1016/j.jacr.2020.09.060
  • Reddy S, Allan S, Coghlan S, et al. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491–497. doi: 10.1093/jamia/ocz192
  • Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bullet World Health Organ. 2020;98(4):251–256. doi: 10.2471/BLT.19.237487
  • Naik N, Hameed BMZ, Shetty DK, et al. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg. 2022;9:862322. doi: 10.3389/fsurg.2022.862322
  • Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139(1):4–15. doi: 10.1093/bmb/ldab016
  • Choudhury A. Toward an ecologically valid conceptual framework for the use of artificial intelligence in clinical settings: need for systems thinking, accountability, decision-making, trust, and patient safety considerations in safeguarding the technology and clinicians. JMIR Hum Factors. 2022;9(2):e35421. doi: 10.2196/35421
  • Schulz WL, Durant TJS, Krumholz HM. Validation and Regulation of Clinical Artificial Intelligence. Clin Chem. 2019;65(10):1336–1337. doi: 10.1373/clinchem.2019.308304
  • Stanfill MH, Marc DT. Health information management: implications of artificial intelligence on healthcare data and information management. Yearb Med Inform. 2019;28(1):056–064. doi: 10.1055/s-0039-1677913
  • Wolf G. Embracing the future: using artificial intelligence in Australian health practitioner regulation. J Law Med. 2020;28(1):21–44.
  • Pashkov VM, Harkusha AO, Harkusha YO. Artificial intelligence in medical practice: regulative issues and perspectives. Wiad Lek. 2020;73(12 cz 2):2722–2727. doi: 10.36740/WLek202012204
  • Harvey HB, Gowda V. Regulatory Issues and Challenges to Artificial Intelligence Adoption. Radiol Clin North Am. 2021;59(6):1075–1083. doi: 10.1016/j.rcl.2021.07.007
  • King TC, Aggarwal N, Taddeo M, et al. Artificial intelligence crime: an Interdisciplinary Analysis of Foreseeable Threats and solutions. Sci Eng Ethics. 2020;26(1):89–120. doi: 10.1007/s11948-018-00081-0
  • Skaria R, Satam P, Khalpey Z. Opportunities and challenges of disruptive innovation in medicine using artificial intelligence. Am J Med. 2020;133(6):e215–e217. doi: 10.1016/j.amjmed.2019.12.016
  • McKay F, Williams BJ, Prestwich G, et al. Artificial intelligence and medical research databases: ethical review by data access committees. BMC Med Ethics. 2023;24(1):49. doi: 10.1186/s12910-023-00927-8
  • Abràmoff MD, Roehrenbeck C, Trujillo S, et al. A reimbursement framework for artificial intelligence in healthcare. npj Digital Med. 2022;5(1):72. doi: 10.1038/s41746-022-00621-w
  • Vidalis T. Artificial Intelligence in Biomedicine: A Legal Insight. Biotech (Basel (Switzerland)). 2021;10(3):15. doi: 10.3390/biotech10030015
  • McKay F, Williams BJ, Prestwich G, et al. Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model. Res Involv Engagem. 2022;8(1):21. doi: 10.1186/s40900-022-00357-7
  • Jia H. Yi Zeng: promoting good governance of artificial intelligence. Natl Sci Rev. 2020;7(12):1954–1956. doi: 10.1093/nsr/nwaa255
  • Kenny LM, Nevin M, Fitzpatrick K. Ethics and standards in the use of artificial intelligence in medicine on behalf of the royal Australian and New Zealand college of radiologists. J Med Imaging Radiat Oncol. 2021;65(5):486–494. doi: 10.1111/1754-9485.13289