Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education

Quinn, Thomas P.; Coghlan, Simon

doi:10.48550/arxiv.2109.02866

Cited by 9 publications

(32 citation statements)

References 17 publications

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“…The practical challenges and barriers of implementing new teaching content, such as the need for the renewal of accreditation or sufficient knowledge of the teaching staff, further reinforce the recommendations of the experts to only facilitate a basic level of knowledge acquisition of AI as part of the medical education [ 24 ]. The experts interviewed for this study agree on the need for opportunities to specialize in AI based on the student’s interest and the requirement for ongoing training programs and extracurricular activities suggested by current publications [ 7 , 20 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews

Weidener¹,

Fischer²

2023

JMIR Med Educ

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Background The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context. Objective We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education. Methods For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data. Results The qualitative content analysis led to the formation of three main categories (“Knowledge,” “Interpretation,” and “Application”) with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one’s related competencies. Conclusions The analyzed expert interviews’ results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews

Weidener¹,

Fischer²

2023

JMIR Med Educ

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“…This emphasis is also echoed by existing guidelines and frameworks regarding AI ethics [30]. Additional recommendations on AI ethics teaching content include "explainability," "liability," and "accountability," which are also considered important by available guidelines [30,40]. On the basis of the analysis of existing publications on teaching AI ethics in medical education, 12 potential subjects were considered for teaching AI ethics.…”

Section: Ai Ethics In Medical Educationmentioning

confidence: 99%

Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education

Weidener,

Fischer

2024

JMIR Med Educ

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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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“…Studies have explored how AI can be integrated into educational frameworks, such as surgical skills training and case-based learning. Some research focuses on practical applications of AI in medical education, such as using AI-driven robotic systems for skill development and knowledge acquisition through simulations and assessments (3,4). However, challenges exist in implementing these changes.…”

Section: Dear Editormentioning

confidence: 99%

“…Medical schools must establish robust partnerships with leading AI research centers or industry professionals. These alliances serve as a guarantee to seamlessly incorporate cutting-edge AI technologies into curriculum and pedagogical approaches, keeping abreast of the most effective practices (2,3).…”

Section: Intelligent Tutoring Systemsmentioning

confidence: 99%

Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations

Masoumian Hosseini,

Masoumain Hosseini,

Qayumi

2024

J Med Edu

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Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education

Cited by 9 publications

References 17 publications

Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews

Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews

Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education

Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations

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