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Evaluating Source-Based Large Language Models for Preclinical Dermatology Education: Comparative Study.

Frank Je-Min Lin1, Sunghun Cho2

  • 1F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, US.

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This summary is machine-generated.

Providing student notes to source-based large language models (LLMs) significantly boosted response consistency in medical education. However, this note-grounding may limit accuracy on complex questions, highlighting challenges for AI in education.

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Cognitive Load Theory (CLT) Applications

Background:

  • Large language models (LLMs) show promise in medical education, aligning with Cognitive Load Theory (CLT).
  • Source-based LLMs using retrieval-augmented generation (RAG) with student notes may enhance learning by grounding responses in familiar material.
  • Dermatology education could benefit from LLMs, but the impact of student-authored notes on LLM performance is unstudied.

Purpose of the Study:

  • To evaluate the accuracy, reproducibility, and similarity of freely available LLMs on dermatology Step 1 questions.
  • To determine if providing student-generated notes to a source-based LLM affects its performance characteristics.

Main Methods:

  • Four LLMs (NotebookLM with/without notes, ChatGPT-4o mini, Gemini 1.5 Flash) were tested on 121 text-based USMLE Step 1 dermatology questions.
  • Evaluations included majority-consensus accuracy, accuracy by difficulty, intertrial reproducibility, and intermodel agreement.
  • Statistical analyses used Cochran's Q, McNemar tests with Benjamini-Hochberg correction, and Fleiss' Kappa for reproducibility and agreement.

Main Results:

  • ChatGPT-4o mini achieved the highest overall accuracy (84.3%).
  • NotebookLM with notes showed superior intertrial reproducibility (κ=0.927) and performed well on easier questions, but less so on difficult ones.
  • NotebookLM without notes had higher omission rates (10.5%), with accuracy improving to 77.8% when omissions were excluded. Intermodel agreement was higher between NotebookLM with notes and ChatGPT-4o mini.

Conclusions:

  • Student-generated notes significantly enhanced source-based LLM response reproducibility, likely due to consistent source retrieval.
  • Note-grounding may hinder performance on complex questions if keywords are absent in the notes, indicating potential RAG retrieval issues.
  • Educational LLMs must balance source utilization, internal reasoning, and appraisal of student notes to address learning gaps effectively.