Conference Program

In the high-stakes environment of Emergency Medicine (EM), the rapid expansion of clinical knowledge threatens to create a "radical monopoly" where learners may become dependent on non-transparent digital tools. The employment of Artificial Intelligence (AI) in this field raises central ethical questions regarding patient safety, clinical accountability, and the reliability of generated information. International guidelines, such as FUTURE-AI and the WHO guidance, emphasize that AI adoption must remain anchored in the bioethical principles of beneficence, non-maleficence, autonomy, and justice. In this context, explicability plays a crucial role, as AI systems must provide outputs that are understandable, traceable, and open to critical scrutiny by human professionals.

Our study strictly adheres to the ethical principles of AI application in medicine, ensuring that technological innovation remains at the service of human responsibility and clinical oversight. This initial version of the app represents a primary validation of purely educational content, aimed at relieving educators of formatting burdens while maintaining scientific accuracy. The strategic goal of our work was to establish a robust ethical and qualitative foundation to subsequently evolve the tool into a clinical decision support system (CDSS) for real-time use in Emergency Departments.

We conducted a two-arm, randomized controlled trial involving 123 evaluations from Italian EM residents and specialists. The study compared a traditional human-driven workflow with an innovative "human-in-the-loop" AI-assisted approach using GPT-4 to generate flashcards validated by external subject matter experts. The adopted methodology directly targeted the risk of AI hallucinations by safeguarding the central role of the human educator as the final authority for clinical validation and responsibility.

The results demonstrate that human-revised, AI-assisted flashcards achieved significantly higher mean scores in relevance (3.97 vs. 3.61), correctness (3.83 vs. 3.62), and clarity (3.86 vs. 3.71). Conversely, human-only content scored higher in informativeness (3.74 vs. 3.72), highlighting that human expertise is essential for providing nuances based on real-world experience and critical alerts.

Our study highlights that the integration of AI into medical education should be understood as a collaborative and ethically grounded process rather than a substitutive one. Framing AI as an initial support tool, systematically followed by expert clinical review, aligns its use with the fundamental principles of biomedical ethics: it promotes beneficence by enhancing decision quality, safeguards non-maleficence through human validation of outputs, preserves professional and learner autonomy by preventing overreliance on opaque systems, and supports justice by encouraging transparent and accountable use of technological resources.

Within this model, AI does not function as a surrogate decision-maker but as a form of ethically supervised cognitive augmentation, strengthening reflective clinical reasoning while ensuring that responsibility, interpretive authority, and patient-centered judgment remain firmly in human hands.