ArticlePRiMER (Leawood, Kan.)2024
Artificial Intelligence-Prompted Explanations of Common Primary Care Diagnoses.
Article in PRiMER (Leawood, Kan.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- AI-generated patient information leaflets for oral anticoagulants: quality, usability, readability, and reproducibility compared with FDA-referenced materials.International journal of clinical pharmacy · 2026Article
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- Response to "Artificial Intelligence-Prompted Explanations of Common Primary Care Diagnoses".PRiMER (Leawood, Kan.) · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI)-generated explanations about medical topics may be clearer and more accessible than traditional evidence-based sources, enhancing patient understanding and autonomy. We evaluated different AI explanations for patients about common diagnoses to aid in patient care. Methods: We prompted ChatGPT 3.5, Google Bard, HuggingChat, and Claude 2 separately to generate a short patient education paragraph about seven common diagnoses. We used the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) to evaluate the readability and grade level of the responses. We used the Agency for Healthcare Research and Quality's Patient Education Materials Assessment Tool (PEMAT) grading rubric to evaluate the understandability and actionability of responses. Results: Claude 2 demonstrated scores of FRE (67.0), FKGL (7.4), and PEMAT, 69% for understandability, and 34% for actionability. ChatGPT scores were FRE (58.5), FKGL (9.3), PEMAT (69% and 31%, respectively). Google Bard scores were FRE (50.1), FKGL (9.9), PEMAT (52% and 23%). HuggingChat scores were FRE (48.7) and FKGL (11.6), PEMAT (57% and 29%). Conclusion: Claude 2 and ChatGPT demonstrated superior readability and understandability, but practical application and patient outcomes need further exploration. This study is limited by the rapid development of these tools with newer improved models replacing the older ones. Additionally, the accuracy and clarity of AI responses is based on that of the user-generated response. The PEMAT grading rubric is also mainly used for patient information leaflets that include visual aids and may contain subjective evaluations.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.