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Enhancing medical education with AI: A comparative study of various large language models in radiology

Truvace record
TRV-2026-2464raw data (JSON)
Version
v1Published
First published
Evidence type
Benchmark or lab evaluation
Sources
1 sourcePeer-reviewed
sha256
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Objective To compare how various Large Language Models (LLMs) perform when solving text- and image-based medical student-level radiology multiple-choice questions (MCQs). Methods We evaluated the accuracy of seven LLMs (i.e., ChatGPT-4o, ChatGPT-5, ChatGPT-5 Plus, DeepSeek-V3.1-Terminus, Microsoft Copilot, Claude 4, and Gemini 2.5 Flash) in solving 60 text-based and 60 image-based MCQs. We compared the seven LLMs' accuracy across question types, domains, and topics. Results The mean accuracy across the seven eva…

The problem

In a test of seven AI chatbots on 120 medical-student radiology questions, average accuracy was 65% on image-based questions versus 95% on text-based ones, so image answers need faculty checking.

What this doesn’t fix

The 120 questions were written at a single institution by two faculty members without pilot testing, there was no human comparison group, and text questions were batched while image questions were not, so the gap may overstate image-reading weakness.

Sources

1 on file
  1. PLOS Onedoi:10.1371/journal.pone.0360928
    Peer-reviewed2026-11-06

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v1

Truvace Impact Record TRV-2026-2464, v1: “Enhancing medical education with AI: A comparative study of various large language models in radiology.” Truvace, 2026-11-11. /record/TRV-2026-2464 (accessed on the day you copy it). sha256 20da5035a06a5599…

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