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Performance of artificial intelligence chatbots in interpreting clinical images of pressure injuries.
Shiraishi, Makoto; Kanayama, Koji; Kurita, Daichi; Moriwaki, Yuta; Okazaki, Mutsumi.
Afiliação
  • Shiraishi M; Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, Tokyo, Japan.
  • Kanayama K; Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, Tokyo, Japan.
  • Kurita D; Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, Tokyo, Japan.
  • Moriwaki Y; Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, Tokyo, Japan.
  • Okazaki M; Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, Tokyo, Japan.
Wound Repair Regen ; 2024 May 15.
Article em En | MEDLINE | ID: mdl-38747443
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Wound Repair Regen Assunto da revista: DERMATOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Japão

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Wound Repair Regen Assunto da revista: DERMATOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Japão