ArticleCureus2024
Assessing the Quality, Readability, and Acceptability of AI-Generated Information in Plastic and Aesthetic Surgery.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- Large language model applications in facial plastic and reconstructive surgery: a systematic review of applications, performance, and ethical considerations.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
- Large Language Models in Medical and Dental Education: A Cross-Sectional Comparison of AI-Generated and Faculty-Authored Prosthodontic Materials.Dentistry journal · 2026Article
- A Comparative Study on the Use of DeepSeek-R1 and ChatGPT-4.5 in Different Aspects of Plastic Surgery.Aesthetic plastic surgery · 2026Article
- Can ChatGPT pass the Turkish Orthopedics and Traumatology Board Examination? Turkish orthopedic surgeons versus artificial intelligence.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2025Observational
- Enhancing Patient Comprehension of Glomerular Disease Treatments Using ChatGPT.Healthcare (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWithin plastic surgery, a patient's most commonly used first point of information before consulting a surgeon is the internet. Free-to-use artificial intelligence (AI) websites like ChatGPT (Generative Pre-trained Transformers) are attractive applications for patient information due to their ability to instantaneously answer almost any query. Although relatively new, ChatGPT is now one of the most popular artificial intelligence conversational software tools. The aim of this study was to evaluate the quality and readability of information given by ChatGPT-4 on key areas in plastic and reconstructive surgery.
methodsThe ten plastic and aesthetic surgery topics with the highest worldwide search volume in the 15 years were identified. These were rephrased into question format to create nine individual questions. These questions were then input into ChatGPT-4. The response quality was assessed using the DISCERN. The readability and grade reading level of the responses were calculated using the Flesch-Kincaid Reading Ease Index and Coleman-Liau Index. Twelve physicians working in a plastic and reconstructive surgery unit were asked to rate the clarity and accuracy of the answers on a scale of 1-10 and state 'yes or no' if they would share the generated response with a patient.
resultsAll answers were scored as poor or very poor according to the DISCERN tool. The mean DISCERN score for all questions was 34. The responses also scored low in readability and understandability. The mean FKRE index was 33.6, and the CL index was 15.6. Clinicians working in plastics and reconstructive surgery rated the questions well in clarity and accuracy. The mean clarity score was 7.38, and the accuracy score was 7.4.
conclusionThis study found that according to validated quality assessment tools, ChatGPT-4 produced low-quality information when asked about popular queries relating to plastic and aesthetic surgery. Furthermore, the information produced was pitched at a high reading level. However, the responses were still rated well in clarity and accuracy, according to clinicians working in plastic surgery. Although improvements need to be made, this study suggests that language models such as ChatGPT could be a useful starting point when developing written health information. With the expansion of AI, improvements in content quality are anticipated.
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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.