ArticleJournal of hand surgery global online2025
Artificial Intelligence Can Answer Postoperative Questions About Distal Radius Fractures-But Can Patients Understand the Answers?
Article in Journal of hand surgery global online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to assess the validity, reliability, and readability of responses to common patient questions about postoperative from ChatGPT, Microsoft Copilot, and Google Gemini. Methods: Twenty-seven thoroughly vetted questions regarding distal radius fractures repair surgery were compiled and entered into ChatGPT 4, Gemini, and Copilot. The responses were analyzed for quality, accuracy, and readability using the DISCERN scale, the Journal of the American Medical Association benchmark criteria, Flesch-Kincaid Reading Ease Score, and Flesch-Kincaid Grade Level. Citations provided by Google Gemini and Microsoft Copilot were further categorized by source of reference. Five questions were resubmitted, requesting response simplification. The responses were re-evaluated using the same metrics. Results: All three artificial intelligence platforms produced answers that were considered "good" quality (DISCERN scores >50). Copilot had the highest quality of information (68.3), followed by Gemini (62.9) and ChatGPT (52.9). The information provided by Copilot demonstrated the highest reliability, with a Journal of the American Medical Association benchmark criterion of 3 (of 4) compared with Gemini (1) and ChatGPT (0). All three platforms generated complex texts with Flesch-Kincaid Reading Ease Scores ranging between 35.8 and 41.4 and Flesch-Kincaid Grade Level scores between 10.5 and 12.1, indicating a minimum of high-school graduate reading level required. After simplification, Gemini's reading level remained unchanged, whereas ChatGPT improved to that of a seventh-grade reading level and Copilot improved to that of an eighth-grade reading level. Copilot had a higher number of references (74) compared with Gemini (36). Conclusions: All three platforms provided safe and reliable answers to postoperative questions about distal radius fractures. High reading levels provided by AI remain the biggest barrier to patient accessibility. Clinical relevance: For the current state of mainstream AI platforms, they are best suited as adjunct tools to support, rather than replace, clinical communication from health care workers.
Indexed as
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.