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AI-generated feedback on clinical reasoning for medical interns

Source article: Comparative evaluation of ChatGPT and Gemini feedback in scenario-based clinical reasoning among medical interns

Objective: Artificial intelligence tools are increasingly used to provide individualized feedback in medical education. The aim of this study was to compare the quality of feedback generated by ChatGPT and Gemini during scenario-based clinical reasoning activities. Methods: A convergent mixed-methods study was conducted with 32 medical interns randomly assigned to ChatGPT (n=16) or Gemini (n=16). Following a virtual patient interview on iron deficiency anemia, artificial intelligence-generated feedback was evalu…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1571· cite & verify
Comparative evaluation of ChatGPT and Gemini feedback in scenario-based clinical reasoning among medical interns
Granada - Hospital Doctor Olóriz 2 by Zarateman. CC0 · http://creativecommons.org/publicdomain/zero/1.0/deed.en
Trace impact readingContested
P 68The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 70The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

Researchers randomly assigned 32 medical interns to receive feedback from either ChatGPT or Gemini after a virtual patient interview on iron deficiency anemia. Interns and medical education specialists then scored the feedback with an 11-item rubric.

The results suggest that general-purpose chatbots can supplement personalized feedback in medical training and may ease faculty workload, but only under human oversight. The small sample and a single clinical scenario leave open how well the findings apply to other cases, settings or tools.

Main points

  1. The study enrolled 32 medical interns, split evenly between ChatGPT (n=16) and Gemini (n=16).
  2. Interns scored ChatGPT 1.80±0.20 and Gemini 1.75±0.26; specialists scored them 1.91±0.06 and 1.87±0.10.
  3. No statistically significant differences were found between the two systems (p>0.05).
  4. ChatGPT feedback was more structured and reflective, while Gemini offered more concrete examples.

The gain

Both chatbots produced feedback that interns and medical education specialists rated as clear, supportive and educationally useful in a virtual iron deficiency anemia case.

The problem

Feedback from both chatbots lacked patient-centeredness and systematicity and sometimes included unrealistic statements, so it cannot replace expert supervision.

The rundown

The design was a convergent mixed-methods study. Quantitative scores were analyzed with descriptive statistics and Mann-Whitney U tests, and qualitative comments were analyzed through content analysis.

The rubric covered clarity, accuracy, structure, identification of strengths and weaknesses, contribution to clinical reasoning, and overall educational value. Raters included both the interns and medical education specialists.

The authors conclude that the tools may support personalized learning and reduce faculty workload when used under human oversight, but they cannot replace expert supervision.

What this doesn’t fix

The study compared the tools in a single virtual scenario with 32 interns and found no significant differences, while both tools shared limits such as weak patient-centeredness and occasional unrealistic statements.

Sources

  1. Peer-reviewedRevista da Associação Médica Brasileira2026-10-12

The debate