Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Use of large language models for providing automated feedback in medical imaging education: a systematic review

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 Introduction Large language models (LLMs) are an emerging form of generative artificial intelligence (AI) with promising applications in medical education, and their ability to provide automated feedback may enhance medical imaging education for trainees. This review aims to systematically examine and synthesize the published literature on the use of LLMs in providing automated feedback in medical imaging education. Methods We conducted this systematic review in accordance with the PRISMA 2020 guidelines. A comprehensive search of the PubMed, Scopus, and Embase databases was conducted, covering studies published through January 2026. Our search strategy included keywords related to “feedback, generative artificial intelligence, large language models, radiology, and medical imaging.” Studies were eligible if they examined the use of LLMs to generate automated feedback for medical trainees within medical imaging education. Extracted data were synthesized using descriptive synthesis, with quality appraisal assessed using ROBINS-I and GRADE. Results Of 1,003 identified records, 7 met the inclusion criteria. All studies examined the applications of automated LLM feedback in the medical education of radiology residents, with one study also including fellows. Reported educational outcomes included enhanced report quality, improved diagnostic accuracy, and increased efficiency in discrepancy detection. LLM feedback was generally well-received among trainees, with learners expressing satisfaction with the LLM feedback and preferring a hybrid human-AI feedback model. Additionally, fine-tuned models generally showed stronger performance than general-purpose LLMs and demonstrated variable agreement with expert-human consensus. Conclusion LLMs show a potentially promising role as supportive tools for providing automated feedback in medical imaging education, alongside human feedback. This includes reported gains in accuracy, efficiency, and learner satisfaction. However, the current published evidence is preliminary and limited. Larger multicenter studies with standardized methods are necessary before widespread adoption can be justified. Our systematic review emphasizes that human expert oversight remains essential, as the current evidence supports preliminary technical feasibility, but not yet definitive educational effectiveness. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251081394, Identifier CRD420251081394 Keywords: automated feedback, generative AI, large language model, medical education, radiology, systematic review

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

Mustafa Mohammed Al-Mashhadani; Faika Ajaz; Shaista Salman Guraya; Farah Ennab. Use of large language models for providing automated feedback in medical imaging education: a systematic review. Frontiers in Medicine (2026-06-10). https://doi.org/10.3389/fmed.2026.1803921

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.