Makoto Shiraishi, Koji Kanayama, Daichi Kurita, Yuta Moriwaki, Mutsumi Okazaki
{"title":"Performance of artificial intelligence chatbots in interpreting clinical images of pressure injuries.","authors":"Makoto Shiraishi, Koji Kanayama, Daichi Kurita, Yuta Moriwaki, Mutsumi Okazaki","doi":"10.1111/wrr.13189","DOIUrl":null,"url":null,"abstract":"<p><p>To evaluate the accuracy of AI chatbots in staging pressure injuries according to the National Pressure Injury Advisory Panel (NPIAP) Staging through clinical image interpretation, a cross-sectional design was conducted to assess five leading publicly available AI chatbots. As a result, three chatbots were unable to interpret the clinical images, whereas GPT-4 Turbo achieved a high accuracy rate (83.0%) in staging pressure injuries, notably outperforming BingAI Creative mode (24.0%) with statistical significance (p < 0.001). GPT-4 Turbo accurately identified Stages 1 (p < 0.001), 3 (p = 0.001), and 4 (p < 0.001) pressure injuries, and suspected deep tissue injuries (p < 0.001), while BingAI demonstrated significantly lower accuracy across all stages. The findings highlight the potential of AI chatbots, especially GPT-4 Turbo, in accurately diagnosing images and aiding the subsequent management of pressure injuries.</p>","PeriodicalId":23864,"journal":{"name":"Wound Repair and Regeneration","volume":" ","pages":"652-654"},"PeriodicalIF":3.8000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wound Repair and Regeneration","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/wrr.13189","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/15 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
To evaluate the accuracy of AI chatbots in staging pressure injuries according to the National Pressure Injury Advisory Panel (NPIAP) Staging through clinical image interpretation, a cross-sectional design was conducted to assess five leading publicly available AI chatbots. As a result, three chatbots were unable to interpret the clinical images, whereas GPT-4 Turbo achieved a high accuracy rate (83.0%) in staging pressure injuries, notably outperforming BingAI Creative mode (24.0%) with statistical significance (p < 0.001). GPT-4 Turbo accurately identified Stages 1 (p < 0.001), 3 (p = 0.001), and 4 (p < 0.001) pressure injuries, and suspected deep tissue injuries (p < 0.001), while BingAI demonstrated significantly lower accuracy across all stages. The findings highlight the potential of AI chatbots, especially GPT-4 Turbo, in accurately diagnosing images and aiding the subsequent management of pressure injuries.
期刊介绍：
Wound Repair and Regeneration provides extensive international coverage of cellular and molecular biology, connective tissue, and biological mediator studies in the field of tissue repair and regeneration and serves a diverse audience of surgeons, plastic surgeons, dermatologists, biochemists, cell biologists, and others.
Wound Repair and Regeneration is the official journal of The Wound Healing Society, The European Tissue Repair Society, The Japanese Society for Wound Healing, and The Australian Wound Management Association.
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