Actionable companion · Assessment and evidence of learning

From evidence to district action: Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Ophthalmology Continuing Medical Education 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.

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 Purpose To evaluate the accuracy and prose responses of 2 large language models (LLMs) to ophthalmology continuing medical education questions. Design Question prompts and multiple choice (MC) answer options were input into the 2 LLMs, and responses were analyzed for accuracy and assessed for evidence of correctness, completeness, bias, and potential harm using a previously reported standardized rubric. Subjects Basic and Clinical Science Course questions and MC answer options from the American Academy of Ophthalmology question bank were used as inputs into the 2 LLMs (ChatGPT-4 and Google Vertex’s Gemini Pro 1.5). Methods The MC responses were assessed for accuracy in comparison to the question bank’s designated corrected answer. The free-text prose responses from the 2 LLMs were assessed by 3 board-certified ophthalmologists. Main Outcome Measures Accuracy and assessment of correct and incorrect reasoning, inappropriate content, missing content, possibility of bias, or possibility of harm. Results The MC accuracy rates of ChatGPT-4 and Gemini Pro 1.5 were 82.5% (99/120) and 49.2% (59/120) (P < 0.05), respectively. Though there was high evidence of correct reasoning in the prose responses (92% and 88% for ChatGPT-4 and Gemini Pro 1.5, respectively), there was also evidence of incorrect reasoning (42% and 58%), inappropriate content (29% and 36%), missing content (42% and 30%), and possibility of physical or emotional harm (36% and 44%). Conclusions Though ChatGPT-4 was able to perform well in MC accuracy, both LLMs contained inaccuracies, missing content, and material that could lead to harm in their prose responses. Our findings suggest that provider-guided auditing in ophthalmology is required before the use of the technology in direct patient-facing settings. Financial Disclosure(s) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. Keywords: Large language models, Accuracy, ChatGPT-4, Gemini Pro 1.5

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

Jacqueline L Chen; Amanda J Lu; Rohan Verma; Li Wang; Douglas D Koch; Allison J Chen. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Ophthalmology Continuing Medical Education Questions. Ophthalmology Science (2026-02-26). https://doi.org/10.1016/j.xops.2026.101130

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.