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Cited by 148Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence in Education

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

Open access
Franco D’Souza, Russell, Mathew, Mary, Mishra, Vedprakash, Surapaneni, Krishna Mohan·2024·Medical Education Online
DOI 10.1080/10872981.2024.2330250PMC PMC10993743
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Abstract

Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.

AI snapshot

Written from the paper’s full text.

TL;DR

This article presents twelve practical tips for medical educators to address ethical concerns when implementing artificial intelligence (AI) in medical education, covering transparency, bias, validation, privacy, consent, collaboration, training, monitoring, accountability, regulation, and ethics oversight. The authors argue that adhering to these tips can support a responsible, sustainable, and ethically grounded integration of AI.

Key findings

As a tips/commentary paper, it offers recommendations rather than empirical results:

  • Transparency requires choosing explainable algorithms and documenting data, training processes, and decision logic, though the authors acknowledge "complete transparency in AI systems is not achievable in all contexts."
  • Bias should be addressed through diverse, representative training data and regular bias audits, while conceding that "eliminating all bias in AI systems may not be fully achievable."
  • AI-generated educational content should be validated by domain experts and end-user feedback as an ongoing, not one-time, process.
  • Privacy protection should use encryption, anonymization where feasible, and compliance with HIPAA; the authors note "complete privacy in AI models cannot be guaranteed" and that personalization may require users to "sacrifice a certain level of privacy."

Methods

This is a narrative, expert-opinion "twelve tips" article; no study design, dataset, or sample is reported. The authors synthesize prior literature (61 references) and illustrate each tip with hypothetical scenarios (e.g., an adaptive learning platform, an AI-driven recommendation system, a virtual escape room, a CDSS training case, an AI radiology curriculum, an AI tutoring system, and an AI surgical simulation). No formal analysis method is described.

Limitations

Not stated in the paper; the article does not include a limitations section. Evident from the text: the recommendations are not empirically tested, and the authors repeatedly acknowledge that full transparency, complete bias elimination, and guaranteed privacy are unattainable goals, framing the tips as aspirational guidance rather than validated interventions.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1080/10872981.2024.2330250

Cites

62 of 62 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 28 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    Medical schools worldwide have not yet explored AI to its full potential with robust implementation.

    “Even though AI has numerous advantages, medical schools worldwide are yet to explore AI to its full potential with robust implementation.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [5] 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 35671077 PMC9214616 source

      Not checked in the free run.

  • Premisecitation

    Monitoring an AI algorithm's performance helps identify potential biases or unintended consequences that may arise during its use.

    “Additionally, monitoring the algorithm’s performance helps identify any potential biases or unintended consequences that may arise during its use [43].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [43] 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 33096088 PMC7574690 source

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  • Interpretationreasoning

    If AI systems are based on algorithms constructed several years ago, they may become outdated and less effective.

    “if these systems are based on algorithms constructed several years ago, they may become outdated and less effective.”
    Quote in the paperSubstantial claim
  • Premisecitation

    AI in medical education lacks transparency in AI decision-making processes, making it difficult for users to understand how conclusions are drawn.

    “the lack of transparency in AI decision-making processes making it difficult for users to understand how conclusions are drawn”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [4] 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 37387115 source

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  • Premisecitation

    Ethical concerns are a key obstacle to the full integration of AI in medical education.

    “Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [4] 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 37387115 source

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  • May combine two claims. 5

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    HIPAA is designed to protect the privacy of individuals' health information while allowing the flow of health information needed to ensure high-quality health care and to protect the public's health and well-being.

    “This is designed to protect the privacy of individuals’ health information while allowing the flow of health information needed to ensure high-quality health care and to protect the public’s health and well-being [24].”
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    • cites [24] Office for Civil Rights, H. H. S. Standards for privacy of individually identifiable health information. Final Rule Federal Register. 2002: 67(157): 53181– 53273. 12180470

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Anonymizing data whenever feasible adds an extra layer of privacy, allowing educational insights without compromising individuals' identities.

    “Anonymizing data whenever feasible adds an extra layer of privacy, allowing educational insights without compromising individuals’ identities [22].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [22] 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 31311603 PMC6636050 source

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  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Complete transparency in AI systems is not achievable in all contexts.

    “However, it’s essential to acknowledge that complete transparency in AI systems is not achievable in all contexts.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Bias in AI algorithms can manifest as selection or information bias, potentially leading to unfair and inaccurate outcomes.

    “This bias in AI algorithms can manifest in various ways such as selection or information bias, potentially leading to unfair and inaccurate outcomes [10].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [10] 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 34550305 PMC9379852 source

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  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    If educators are not aware of AI ethics, it leads to improper and irresponsible use of CDSS for teaching purposes and paves the way for students to misuse CDSS in future without proper guidelines on its ethical use.

    “If educators are not aware, it not only leads to improper and irresponsible use of CDSS for teaching purposes but also paves the way for students to misuse CDSS in future without having proper guidelines on its ethical use.”
    Quote in the paperNot a substantial claim

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    Interpretationreasoning

    Anonymization is not the only way to protect user data always.

    “However, anonymization is not the only way to protect user data always.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Complete privacy in AI models cannot be guaranteed because of changing risks and complex data issues.

    “Even though complete privacy in AI models cannot be guaranteed because of changing risks and complex data issues, the aim should be set to keep information as safe as possible.”
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    Premisecitation

    Establishing a transparent framework for accountability mitigates risks.

    “Establishing such a transparent framework for accountability not only mitigates risks but also fosters a culture of responsibility and collaboration among all involved parties [48].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [48] 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 35727615 PMC9257623 source

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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    A forward-looking approach to tracking and adhering to emerging regulations ensures that the integration of AI into medical education aligns with evolving legal frameworks.

    “This forward-looking approach ensures that the integration of AI into medical education aligns with evolving legal frameworks, thereby safeguarding patient data, ensuring privacy, and upholding the ethical standards essential to responsible AI deployment in healthcare settings [53].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [53] 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 34689875 source

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    Premisecitation

    Establishing a transparent framework for accountability fosters a culture of responsibility and collaboration among all involved parties.

    “Establishing such a transparent framework for accountability not only mitigates risks but also fosters a culture of responsibility and collaboration among all involved parties [48].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [48] 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 35727615 PMC9257623 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation

    Alignment of AI integration in medical education with evolving legal frameworks safeguards patient data, ensures privacy, and upholds ethical standards essential to responsible AI deployment in healthcare settings.

    “This forward-looking approach ensures that the integration of AI into medical education aligns with evolving legal frameworks, thereby safeguarding patient data, ensuring privacy, and upholding the ethical standards essential to responsible AI deployment in healthcare settings [53].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [53] 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 34689875 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Eliminating all bias in AI systems may not be fully achievable.

    “However, just like humans, machines also have implicit bias and eliminating all bias in AI systems may not be fully achievable, but it is imperative to strive towards minimizing biases to ensure fairness and equity in AI applications.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Involving students in discussions provides valuable input into the development process, helping to tailor AI applications to meet their needs and preferences.

    “Involving students in these discussions provides valuable input into the development process, helping to tailor AI applications to meet their needs and preferences [31].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] 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 32755431 source

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    Premisecitation

    Implementation of advanced data encryption techniques plays a crucial role in protecting individual identities and maintaining the integrity of personal and health-related data.

    “Implementation of advanced data encryption techniques plays a crucial role in protecting individual identities and maintaining the integrity of personal and health-related data [20,21].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [20] 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 35612030 source

      Not checked in the free run.

    • cites [21] 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 34525993 PMC8442400 source

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    Premisecitation

    Assigning specific roles to AI developers, educators, administrators, and other stakeholders including students ensures that everyone understands their responsibilities and is accountable for the ethical and effective use of AI in education.

    “Assigning specific roles to AI developers, educators, administrators, and other stakeholders including students ensures that everyone understands their responsibilities and is accountable for the ethical and effective use of AI in education [46].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [46] 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 35360424 PMC8963864 source

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  • Classified as probably not a substantial claim.

    Premisecitation

    Obtaining written informed consent is a foundational step in the ethical integration of AI technologies into educational as well as clinical processes.

    “Obtaining written informed consent is a foundational step in the ethical integration of AI technologies into educational as well as clinical processes [26].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [26] 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 33256715 PMC7706019 source

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  • Classified as probably not a substantial claim.

    Premisecitation

    Ethics committees play a crucial role in stressing the importance of adherence to regulation and scrutinizing potential ethical challenges, including privacy concerns, fairness, and transparency.

    “Though not martinet, the ethics committees play a crucial role in stressing the importance of adherence to regulation and scrutinizing potential ethical challenges, including privacy concerns, fairness, and transparency [57].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [57] 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 PMC9184542 35681002 source

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    Premisecitation

    Regular collaboration with ethical oversight bodies ensures that any emerging ethical concerns are identified and addressed promptly.

    “Regular collaboration with these ethical oversight bodies ensures that any emerging ethical concerns are identified and addressed promptly [60].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [60] Jia H. Yi Zeng: promoting good governance of artificial intelligence. Natl Sci Rev. 2020; 7(12): 1954– 1956. doi: 10.1093/nsr/nwaa255 34676086 PMC7665600 source

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    Premisecitation

    The approach of forming an ethics committee or engaging existing institutional review boards safeguards against potential ethical pitfalls.

    “This approach not only safeguards against potential ethical pitfalls but also contributes to the establishment of ethical best practices, promoting responsible and accountable use of AI in medical education [61].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [61] 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 34342139 PMC9291743 source

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  • Classified as probably not a substantial claim.

    Premisecitation

    Engaging institutional review boards leverages their expertise in ethical review processes, particularly when handling sensitive data or novel applications of AI.

    “Engaging institutional review boards leverages their expertise in ethical review processes, particularly when handling sensitive data or novel applications of AI [59].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [59] 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 35598004 PMC9123617 source

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    Premisecitation

    The approach of forming an ethics committee or engaging existing institutional review boards contributes to the establishment of ethical best practices, promoting responsible and accountable use of AI in medical education.

    “This approach not only safeguards against potential ethical pitfalls but also contributes to the establishment of ethical best practices, promoting responsible and accountable use of AI in medical education [61].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [61] 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 34342139 PMC9291743 source

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21 of 21

  • The paper relies on this without support.

    Biases in AI algorithms can lead to unequal representation and impact the quality of medical education.

    “Biases in AI algorithms can lead to unequal representation and impact the quality of medical education.”
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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • To effectively harness the technology, it is essential to develop guidelines that promote innovative teaching strategies and assessments.

    “To effectively harness the technology, it is essential to develop guidelines that promote innovative teaching strategies and assessments [44].”

    From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [44]

    The Introduction says AI can support technology-enhanced teaching and adaptive assessments, and identifies a lack of standard guidelines for responsible AI use as a barrier to effective integration. The Conclusion also endorses ethical guidelines to harness AI’s potential. However, the paper does not specifically call for developing guidelines that promote innovative teaching strategies and assessments. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • A lack of empirical research and foundational definitions of AI ethics was noted as a barrier to guiding curriculum development.

    “A lack of empirical research and foundational definitions of AI ethics was noted as a barrier to guiding curriculum development [190, 195, 198–200].”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [195]

    The Introduction (page unavailable) identifies a lack of standard guidelines for responsible AI use and faculty AI expertise as barriers to robust implementation in medical education. It does not identify a lack of empirical research or foundational definitions of AI ethics, or specifically link those gaps to curriculum development. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Numerous articles (5%) provide guidelines and "tips" on AI in medical education.

    “Numerous articles (5%) provide guidelines and “tips” on AI in medical education [57, 83, 94, 135, 136, 162, 179, 186, 195, 196, 202, 205–210].”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [195]

Cites

Flagged first, then by citations

1 of the 63 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. 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.

Cited by

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Related

Authors
Franco D’Souza, R., Mathew, M., Mishra, V., & Surapaneni, K. M.
Affiliations
  • Department of Education, UNESCO Chair in Bioethics, Melbourne, Australia
  • Manipal Academy of Higher Education (IN)
  • Datta Meghe Institute of Higher Education and Research (IN)
  • Panimalar Medical College Hospital & Research Institute (IN)
Corresponding author
Surapaneni, Krishna Mohan
ORCID
Venue
Medical Education Online
Publisher
Informa UK Limited
Published
2024-04-03
Volume
29
Issue
1
Pages
2330250-2330250
ISSN
1087-2981 (ISSN-L)
DOI
10.1080/10872981.2024.2330250
PMC
PMC10993743
OpenAlex
W4393862318
Cited by
147
References
63
Citations by year
2026: 57 · 2025: 69 · 2024: 20 · 2020: 1
FWCI
3.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Medical Education Online (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
no funding
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
artificial intelligence in medical education, AI ethics, transparency, algorithmic bias, data protection, informed consent, teacher professional development, ethics committees, accountability, AI monitoring, responsible AI adoption
MeSH terms
Artificial Intelligence, Education, Medical, Humans, Social Responsibility, Students

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