Actionable companion · Assessment and evidence of learning

From evidence to district action: Evaluating the reliability, quality, and readability of AI-generated patient education on hallux valgus: a comparative study of large language models

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:

Background Patients with hallux valgus increasingly seek health information through consumer-facing artificial intelligence (AI)–driven patient education tools, particularly large language model–based conversational agents. Although these tools offer rapid and accessible responses, concerns remain regarding the reliability, usefulness, overall quality, and readability of AI-generated patient education materials. Evidence specifically evaluating AI-generated patient education for hallux valgus, a condition strongly influenced by patient expectations and treatment preferences, remains limited. Methods This cross-sectional comparative study evaluated the performance of three large language models—ChatGPT-4o, Gemini-2.5-Flash, and DeepSeek-V3—in responding to 20 patient-centered questions related to hallux valgus. Questions were developed using AI-assisted question generation and publicly available Google Trends search patterns and categorized into four clinical domains. AI-generated responses were anonymized and independently assessed by three orthopaedic surgeons for reliability, usefulness, and overall quality using 7-point Likert-based reliability and usefulness scales and the Global Quality Scale (GQS). Readability was analyzed using six standardized indices. Inter-rater agreement and between-model comparisons were statistically evaluated. Results Gemini-2.5-Flash demonstrated modestly higher overall reliability, particularly in questions related to etiology and clinical presentation. DeepSeek-V3 achieved higher usefulness scores in the long-term outcomes and quality-of-life domain and produced significantly more readable content, as reflected by higher Flesch Reading Ease scores and lower grade-level indices. In contrast, Gemini-2.5-Flash generated linguistically more complex responses requiring higher educational levels for comprehension. Overall usefulness and global quality scores did not differ significantly among models. Qualitative review also identified occasional examples of oversimplified or potentially misleading information. Despite these differences, several readability metrics exceeded recommended patient health-literacy thresholds. Conclusions Contemporary AI-based conversational agents can provide patient-oriented information regarding hallux valgus with variable reliability and readability characteristics, although statistically significant differences were observed across models. A trade-off between factual accuracy and linguistic accessibility was observed. AI tools should therefore be regarded as adjuncts to, rather than replacements for, clinician-led patient education. Awareness of AI limitations and appropriate clinical guidance remain essential to ensure safe, accurate, and patient-centered information delivery. Clinical trial number Not applicable. Supplementary Information The online version contains supplementary material available at https://doi.org/10.1186/s12911-026-03722-8.

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

Ali Can Koluman; Ebru Aloglu Ciftci; Mehmet Utku Ciftci; Başar Burak Cakmur; Nezih Ziroglu. Evaluating the reliability, quality, and readability of AI-generated patient education on hallux valgus: a comparative study of large language models. BMC Medical Informatics and Decision Making (2026-07-30). https://doi.org/10.1186/s12911-026-03722-8

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.