JMIR Publications

ABSTRACT

Background:

Medical students increasingly encounter artificial intelligence (AI) and explainable AI (XAI) in clinical training, yet their mental models of diagnostic decision-making and expectations for AI support remain poorly understood. Understanding these expectations is crucial for designing human-centered clinical decision support systems that are pedagogically effective and safe.

Objective:

This study explores medical students' diagnostic processes in their final year of undergraduate training and AI support preferences across four research questions: (1) What information do medical students use to make a diagnosis? (2) In what form would they like (X)AI to support them in this process? (3) What questions do they have for such (X)AI? (4) What form of presentation would they prefer for XAI?

Methods:

Semi-structured interviews (N=14 medical students, ages 24-31) were analyzed using reflexive thematic analysis, following information power principles. Participants worked through a standardized interactive online CASUS™ case based on a real multimorbid patient. During the CASUS™ case, participants used think-aloud techniques, followed by semi-structured interviews and a drawing task in which they sketched desired XAI interfaces for four modalities (laboratory results, electrocardiograms, radiographs, patient photographs). Interviews were audio-recorded, transcribed, and analyzed using an iterative, mixed deductive–inductive content analysis with a collaboratively developed codebook.

Results:

(1) Students prioritized patient-led symptom narratives, progression patterns, comorbidities, and physical signs during the diagnostic process. (2) They sought diagnostic-specific AI support: guideline prompts, ranked differentials (including rare diseases), test checklists, multimodal interpretation, and therapy verification. (3) Trust in AI prerequisites included data origins and specificity metrics. Fears regarding AI centered on loss of control, while benefits included time savings. (4) Preferred XAI featured step-by-step mentor guidance, counterfactuals, similar cases, and chat-based learning triggered by AI actions, surprises, or knowledge gaps.

Conclusions:

Final-year medical students conceive AI not only as a diagnostic assistant but also as a teaching partner that should scaffold reasoning, broaden differentials, and support confidence calibration. Their preferences yield concrete design requirements for trainee-oriented clinical AI/XAI: longitudinal workflow support, layered and multimodal explanations, and explicit communication of uncertainty and limitations to mitigate automation bias. While limited by its single-center, small-sample design and simulated case setting, this study offers actionable insights for the design and evaluation of human-centered AI tools in medical education and motivates prospective studies in authentic clinical learning environments.