ArticleJMIR medical education2024
Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education.
Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The impact of generative artificial intelligence tools on assessment equity for non-native English-speaking medical students: a systematic review.BMC medical education · 2026Pooled it
- Artificial intelligence for affective-domain development in healthcare professions education: a systematic review.Frontiers in medicine · 2026Pooled it
- Artificial intelligence for medical imaging education: bibliometric and visual analysis.Frontiers in medicine · 2026Pooled it
- Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence.Frontiers in medicine · 2026Pooled it
- Principles for medical students' responsible use of generative AI: a student-partnered Delphi study.BMC medical ethics · 2026Article
- Teaching AI ethics in medical schools: a scoping review protocol on the ethical-technical balance in curricular frameworks.BMJ open · 2026Article
- Article
- Medical students' and faculty members' perceptions and experiences of AI integration in health care practice and in medical curricula: A meta-ethnographic review.Medical education · 2026Review
- Enhancing dental clinical education through AI: insights into post-clinical summaries.BMC medical education · 2026Article
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- Evaluation of a Problem-Based Learning Program's Effect on Artificial Intelligence Ethics Among Japanese Medical Students: Mixed Methods Study.JMIR medical education · 2026Article
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Promise to Practice: Reimagining Artificial Intelligence for Equitable Global Health Impact.Annals of global health · 2026Review
- A dialectical lens for AI and medical humanities: advancing responsible augmented humanism in Digital Public Health.Frontiers in public health · 2026Review
- Integrating Artificial Intelligence into Medical Education in LMICs: A Narrative Review.Advances in medical education and practice · 2026Review
- Twelve tips for developing and implementing AI curriculum for undergraduate medical education.Medical education online · 2025Review
- Assessing LLM-generated vs. expert-created clinical anatomy MCQs: a student perception-based comparative study in medical education.Medical education online · 2025Article
- Rethinking gene-edited crop regulation: advancing a Principle-based framework for modern biotechnology governance.GM crops & food · 2025Review
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.