ChatGPT for addressing patient-centred frequently asked questions in glaucoma clinical practice.

artificial intelligence chatbot collaborative care conversation agents large language models

Journal

Ophthalmology. Glaucoma
ISSN: 2589-4196
Titre abrégé: Ophthalmol Glaucoma
Pays: United States
ID NLM: 101730510

Informations de publication

Date de publication:
16 Oct 2024
Historique:
received: 16 07 2024
revised: 17 09 2024
accepted: 11 10 2024
medline: 19 10 2024
pubmed: 19 10 2024
entrez: 18 10 2024
Statut: aheadofprint

Résumé

Large language models such as ChatGPT-3.5 are often used by the public to answer questions related to daily life, including health advice. This study evaluated the responses of ChatGPT-3.5 in answering patient-centred frequently asked questions (FAQs) relevant in glaucoma clinical practice. Prospective cross-sectional survey. Twelve experts across a range of clinical, education and research practices in optometry and ophthalmology. Over 200 patient-centric FAQs from authoritative professional society, hospital and advocacy websites were distilled and filtered into 40 questions across four themes: definition and risk factors, diagnosis and testing, lifestyle and other accompanying conditions, and treatment and follow-up. The questions were individually input into ChatGPT-3.5 to generate responses. The responses were graded by the twelve experts individually. A 5-point Likert scale (1 = strongly disagree; 5 = strongly agree) was used to grade ChatGPT-3.5 responses across four domains: coherency, factuality, comprehensiveness, and safety. Across all themes and domains, median scores were all 4 ("agree"). Comprehensiveness had the lowest scores across domains (mean 3.7±0.9), followed by factuality (mean 3.9±0.9), and coherency and safety (mean 4.1±0.8 for both). Examination of the individual 40 questions showed that 8 (20%), 17 (42.5%), 24 (60%) and 8 (20%) of the questions had average scores below 4 (i.e. below "agree") for the coherency, factuality, comprehensiveness and safety domains, respectively. Free-text comments by the experts highlighted omissions of facts and comprehensiveness (e.g. secondary glaucoma) and remarked on the vagueness of some responses (i.e. that the response did not account for individual patient circumstances). ChatGPT-3.5 responses to FAQs in glaucoma were generally agreeable in terms of coherency, factuality, comprehensiveness, and safety. However, areas of weakness were identified, precluding recommendations for routine use to provide patients with tailored counselling in glaucoma, especially with respect to development of glaucoma and its management.

Identifiants

pubmed: 39424063
pii: S2589-4196(24)00183-2
doi: 10.1016/j.ogla.2024.10.005
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Henrietta Wang (H)

School of Optometry and Vision Science, University of New South Wales, Kensington, New South Wales, Australia; Centre for Eye Health, University of New South Wales, Kensington, New South Wales, Australia.

Katherine Masselos (K)

Centre for Eye Health, University of New South Wales, Kensington, New South Wales, Australia; Department of Ophthalmology, Prince of Wales Hospital, Randwick, New South Wales, Australia.

Janelle Tong (J)

School of Optometry and Vision Science, University of New South Wales, Kensington, New South Wales, Australia; Centre for Eye Health, University of New South Wales, Kensington, New South Wales, Australia.

Heather R M Connor (HRM)

School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia; The Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.

Janelle Scully (J)

Australian College of Optometry, Carlton, Victoria, Australia.

Sophia Zhang (S)

School of Optometry and Vision Science, University of New South Wales, Kensington, New South Wales, Australia; Centre for Eye Health, University of New South Wales, Kensington, New South Wales, Australia.

Daniel Rafla (D)

School of Optometry and Vision Science, University of New South Wales, Kensington, New South Wales, Australia.

Matteo Posarelli (M)

Department of Ophthalmology, Liverpool University Hospitals, Liverpool, UK.

Jeremy C K Tan (JCK)

Department of Ophthalmology, Prince of Wales Hospital, Randwick, New South Wales, Australia; Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, Australia.

Ashish Agar (A)

Centre for Eye Health, University of New South Wales, Kensington, New South Wales, Australia; Department of Ophthalmology, Prince of Wales Hospital, Randwick, New South Wales, Australia; Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, Australia.

Michael Kalloniatis (M)

School of Optometry and Vision Science, University of New South Wales, Kensington, New South Wales, Australia; School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia; University of Houston College of Optometry, Houston, Texas, USA.

Jack Phu (J)

School of Optometry and Vision Science, University of New South Wales, Kensington, New South Wales, Australia; Centre for Eye Health, University of New South Wales, Kensington, New South Wales, Australia; Faculty of Medicine and Health, University of Sydney, Sydney, NSW; Concord Clinical School, Concord Repatriation General Hospital, Concord, NSW. Electronic address: jack.phu@unsw.edu.au.

Classifications MeSH