Ethical Considerations for AI in Medical Education: A Scoping Review

AI-enabled tools offer unprecedented opportunities to enhance medical education. These technologies can generate novel content, create realistic simulations, develop digital patient scenarios, and provide immediate, personalized feedback, thereby enriching student learning experiences. For instance, AI can simulate patient interactions, assist in research and academic writing, generate case studies, and offer interactive tutorials. Tools like ChatGPT can facilitate comprehension and engagement, while graphical AI like Dall-E can produce essential medical visuals for disciplines such as radiology. Machine learning and deep learning algorithms are instrumental in creating interactive training environments for image analysis, and AI-based virtual labs allow for in-depth exploration of anatomical structures. Adaptive learning platforms further personalize instruction by tailoring content to individual student progress, ultimately leading to improved understanding and retention of complex medical concepts.

Various ethical theories offer valuable lenses through which to examine the integration of AI in medical education. From a deontological standpoint, the emphasis on authentic student work means that relying heavily on AI for assignment completion could be considered unethical. Educators also bear a duty to ensure ethical knowledge transfer, which could be compromised by excessive student reliance on AI. Consequentialism suggests evaluating AI use based on its outcomes; while AI might offer time-saving benefits, potential harm to student learning could outweigh these advantages. Care ethics underscores the importance of relational aspects in decision-making, implying that AI applications must demonstrate care for all stakeholders. Actions involving AI that negatively impact student learning or disregard the educator's role would conflict with this framework. Ultimately, education itself can foster responsible innovation by centering ethical guidelines for AI use within curriculum planning and the thoughtful integration of AI-driven tools.

The literature search was conducted over three months, from August to October 2023. Three research team members executed the initial search across three databases: PubMed, Embase, and Web of Science, utilizing Medical Subject Headings (MeSH) terms and specific keywords. Potential articles were meticulously recorded in shared Microsoft Excel spreadsheets. The search strings were designed to capture articles related to medical education, AI, ethics, and specifically ChatGPT in healthcare education. From an initial 193 database search results, titles and abstracts were screened. Duplicate records were identified and removed, resulting in 117 unique articles. These were then independently double-screened by two team members, with any discrepancies resolved through consensus meetings. Thirty-five articles were excluded for not meeting the predefined inclusion and exclusion criteria, leaving a final selection of 82 articles for data extraction and synthesis. The screening process was visualized in a flowchart.

The ethical considerations identified in the literature are largely framed around fundamental ethical principles: autonomy, beneficence, non-maleficence, justice, and transparency. Specific concerns raised by the articles include issues related to privacy of student data, potential biases embedded within AI algorithms, the necessity of informed consent for AI use, and the transparency of algorithmic decision-making processes. Furthermore, the accuracy and reliability of AI-generated content are also significant ethical points of discussion. Ethically mindful use of AI necessitates employing models based on precise and dependable data, and grounding decision-making in transparency and comprehensibility. These ethical challenges have direct implications for how AI tools are developed, implemented, and regulated within medical education settings.

This scoping review highlights several emerging themes and notable gaps in the literature, paving the way for future research. Key opportunities include refining standardized competencies for AI in medical education, developing evidence-based curricula that effectively integrate AI ethically, and conducting longitudinal studies to assess the long-term impact of AI on learning outcomes and professional development. The research underscores the critical juncture medical education faces with the advent of AI, presenting both transformative advancements and significant risks. By addressing the identified ethical challenges and pursuing targeted research, medical education can harness the power of AI to enhance learning experiences and prepare competent, ethically-minded healthcare professionals for the future.