Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Performance of multimodal large language models on image‐based surgical anatomy, anatomical pathology, and radiology questions

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Abstract Multimodal large language models (LLMs) are now deeply integrated into medical education and widely used by medical students, yet it remains unclear whether current models possess the accuracy and reliability needed to support image‐based learning. We evaluated four state‐of‐the‐art multimodal LLMs (ChatGPT‐5.1, Gemini‐2.5, Grok‐4, Claude Sonnet‐4.5) on 208 image‐based examination questions from a Doctor of Medicine program, spanning anatomical pathology (histopathology; 47.6%), radiology (31.7%), and surgical anatomy (20.7%). To isolate visual reasoning, all items were presented in image‐only form with contextual information removed. Items covered seven organ systems, included both constructed‐response and selected‐response formats, and were categorized as recognition‐only or recognition‐plus‐reasoning. ChatGPT‐5.1 achieved the highest accuracy (75.5%; 95% CI [69.2–80.8]), followed by Gemini‐2.5 (59.6%; 95% CI [52.8–66.1]), Claude Sonnet‐4.5 (41.8%; 95% CI [35.3–48.6]), and Grok‐4 (34.6%; 95% CI [28.5–41.3]). Overall model performance differed significantly (p Gemini > Claude ≈ Grok) across different categories. Accuracy was uniformly higher for recognition‐only and selected‐response items. Even the best‐performing model, ChatGPT‐5.1, answered approximately one in four questions incorrectly. These findings suggest that current multimodal LLMs cannot yet replace expert teaching in image‐based learning. Their use in medical education should therefore remain supervised and critically appraised, serving as adjuncts rather than authoritative sources. Keywords: anatomy, assessment, histopathology, large language models, medical education, multimodal AI, radiology

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Ming Lu; Josiah Cheng; Vinod Gopalan. Performance of multimodal large language models on image‐based surgical anatomy, anatomical pathology, and radiology questions. Anatomical Sciences Education (2026-05-13). https://doi.org/10.1002/ase.70256

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.