ArticleSurgery open science2025
Dall-E in hand surgery: Exploring the utility of ChatGPT image generation.
Article in Surgery open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Customized Education Sheets Generated by ChatGPT Improve Parental Visit Satisfaction and Procedural Knowledge Prior to Pediatric Cardiac Catheterization.Pediatric cardiology · 2026Article
- AI-generated patient information leaflets for oral anticoagulants: quality, usability, readability, and reproducibility compared with FDA-referenced materials.International journal of clinical pharmacy · 2026Article
- Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review.NPJ digital medicine · 2026Article
- Dall-E in hand surgery: Exploring the utility of ChatGPT image generation: Correspondence.Surgery open science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has significantly influenced various medical fields, including plastic surgery. Large language model (LLM) chatbots such as ChatGPT and text-to-image tools like Dall-E and GPT-4o are gaining broader adoption. This study explores the capabilities and limitations of these tools in hand surgery, focusing on their application in patient and medical education. Methods: Utilizing Google Trends data, common search terms were identified and queried on ChatGPT-4.5 and ChatGPT-3.5 from the following categories: "Hand Anatomy", "Hand Fracture", "Hand Joint Injury", "Hand Tumor", and "Hand Dislocation". Responses were graded on a 1-5 scale for accuracy and evaluated using the Flesch-Kincaid Grade Level, Patient Education Materials Assessment Tool (PEMAT), and DISCERN instrument. GPT 4o, DALL-E 3, and DALL-E 2 illustrated visual representations of selected ChatGPT responses in each category, which were further evaluated. Results: ChatGPT-4.5 achieved a DISCERN overall score of 3.80 ± 0.23. Its responses averaged 91.67 ± 0.29 for PEMAT understandability and 54.67 ± 0.55 for actionability. Accuracy was 4.47 ± 0.52, with a Flesch-Kincaid Grade Level of 9.26 ± 1.04. ChatGPT-4.5 consistently outperformed ChatGPT-3.5 across all evaluation metrics. For text-to-image generation, GPT-4o produced more accurate visuals compared to DALL-E 3 and DALL-E 2. Conclusions: This study highlights the strengths and limitations of ChatGPT-4.5 and GPT-4o in hand surgery education. While combining accurate text generation with image creation shows promise, these AI tools still need further refinement before widespread clinical adoption.
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.