Assessing the efficacy of artificial intelligence to provide peri-operative information for patients with a stoma

被引:2
作者
Lim, Bryan [1 ]
Lirios, Gabriel [1 ]
Sakalkale, Aditya [2 ]
Satheakeerthy, Shriranshini [2 ]
Hayes, Diana [1 ]
Yeung, Justin M. C. [1 ,2 ]
机构
[1] Western Hlth, Dept Colorectal Surg, Melbourne, Australia
[2] Univ Melbourne, Dept Surg, Western Precinct, Melbourne, Australia
关键词
artificial intelligence; colorectal surgery; education; large language models; stoma; HEALTH; EDUCATION; DISCERN;
D O I
10.1111/ans.19337
中图分类号
R61 [外科手术学];
学科分类号
摘要
BackgroundStomas present significant lifestyle and psychological challenges for patients, requiring comprehensive education and support. Current educational methods have limitations in offering relevant information to the patient, highlighting a potential role for artificial intelligence (AI). This study examined the utility of AI in enhancing stoma therapy management following colorectal surgery.Material and MethodsWe compared the efficacy of four prominent large language models (LLM)-OpenAI's ChatGPT-3.5 and ChatGPT-4.0, Google's Gemini, and Bing's CoPilot-against a series of metrics to evaluate their suitability as supplementary clinical tools. Through qualitative and quantitative analyses, including readability scores (Flesch-Kincaid, Flesch-Reading Ease, and Coleman-Liau index) and reliability assessments (Likert scale, DISCERN score and QAMAI tool), the study aimed to assess the appropriateness of LLM-generated advice for patients managing stomas.ResultsThere are varying degrees of readability and reliability across the evaluated models, with CoPilot and ChatGPT-4 demonstrating superior performance in several key metrics such as readability and comprehensiveness. However, the study underscores the infant stage of LLM technology in clinical applications. All responses required high school to college level education to comprehend comfortably. While the LLMs addressed users' questions directly, the absence of incorporating patient-specific factors such as past medical history generated broad and generic responses rather than offering tailored advice.ConclusionThe complexity of individual patient conditions can challenge AI systems. The use of LLMs in clinical settings holds promise for improving patient education and stoma management support, but requires careful consideration of the models' capabilities and the context of their use.
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页数:33
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共 29 条
  • [1] Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review
    Abd-Alrazaq, Alaa A.
    Alajlani, Mohannad
    Ali, Nashva
    Denecke, Kerstin
    Bewick, Bridgette M.
    Househ, Mowafa
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (01)
  • [2] How to achieve trustworthy artificial intelligence for health
    Baeroe, Kristine
    Miyata-Sturm, Ainar
    Henden, Edmund
    [J]. BULLETIN OF THE WORLD HEALTH ORGANIZATION, 2020, 98 (04) : 257 - 262
  • [3] Ethical considerations about artificial intelligence for prognostication in intensive care
    Beil, Michael
    Proft, Ingo
    van Heerden, Daniel
    Sviri, Sigal
    van Heerden, Peter Vernon
    [J]. INTENSIVE CARE MEDICINE EXPERIMENTAL, 2019, 7 (01)
  • [4] The black box problem revisited. Real and imaginary challenges for automated legal decision making
    Brozek, Bartosz
    Furman, Michal
    Jakubiec, Marek
    Kucharzyk, Bartlomiej
    [J]. ARTIFICIAL INTELLIGENCE AND LAW, 2024, 32 (02) : 427 - 440
  • [5] DISCERN: an instrument for judging the quality of written consumer health information on treatment choices
    Charnock, D
    Shepperd, S
    Needham, G
    Gann, R
    [J]. JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH, 1999, 53 (02) : 105 - 111
  • [6] Preoperative intensive, community-based vs. traditional stoma education:: A randomized, controlled trial
    Chaudhri, S
    Brown, L
    Hassan, I
    Horgan, AF
    [J]. DISEASES OF THE COLON & RECTUM, 2005, 48 (03) : 504 - 509
  • [7] Legal concerns in health-related artificial intelligence: a scoping review protocol
    Da Silva, Michael
    Horsley, Tanya
    Singh, Devin
    Da Silva, Emily
    Ly, Valentina
    Thomas, Bryan
    Daniel, Ryan C.
    Chagal-Feferkorn, Karni A.
    Iantomasi, Samantha
    White, Kelli
    Kent, Arianne
    Flood, Colleen M.
    [J]. SYSTEMATIC REVIEWS, 2022, 11 (01)
  • [8] ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations
    Dave, Tirth
    Athaluri, Sai Anirudh
    Singh, Satyam
    [J]. FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2023, 6
  • [9] Davenport Thomas, 2019, Future Healthc J, V6, P94, DOI 10.7861/futurehosp.6-2-94
  • [10] ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?
    Egli, Adrian
    [J]. CLINICAL INFECTIOUS DISEASES, 2023, 77 (09) : 1322 - 1328