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Comparison of large language models in management advice for melanoma: Google's AI BARD, BingAI and ChatGPT.

Authors :
Mu X
Lim B
Seth I
Xie Y
Cevik J
Sofiadellis F
Hunter-Smith DJ
Rozen WM
Source :
Skin health and disease [Skin Health Dis] 2023 Nov 28; Vol. 4 (1), pp. e313. Date of Electronic Publication: 2023 Nov 28 (Print Publication: 2024).
Publication Year :
2023

Abstract

Large language models (LLMs) are emerging artificial intelligence (AI) technology refining research and healthcare. Their use in medicine has seen numerous recent applications. One area where LLMs have shown particular promise is in the provision of medical information and guidance to practitioners. This study aims to assess three prominent LLMs-Google's AI BARD, BingAI and ChatGPT-4 in providing management advice for melanoma by comparing their responses to current clinical guidelines and existing literature. Five questions on melanoma pathology were prompted to three LLMs. A panel of three experienced Board-certified plastic surgeons evaluated the responses for reliability using reliability matrix (Flesch Reading Ease Score, the Flesch-Kincaid Grade Level and the Coleman-Liau Index), suitability (modified DISCERN score) and comparing them to existing guidelines. t -Test was performed to calculate differences in mean readability and reliability scores between LLMs and p value <0.05 was considered statistically significant. The mean readability scores across three LLMs were same. ChatGPT exhibited superiority with a Flesch Reading Ease Score of 35.42 (±21.02), Flesch-Kincaid Grade Level of 11.98 (±4.49) and Coleman-Liau Index of 12.00 (±5.10), however all of these were insignificant ( p  > 0.05). Suitability-wise using DISCERN score, ChatGPT 58 (±6.44) significantly ( p  = 0.04) outperformed BARD 36.2 (±34.06) and was insignificant to BingAI's 49.8 (±22.28). This study demonstrates that ChatGPT marginally outperforms BARD and BingAI in providing reliable, evidence-based clinical advice, but they still face limitations in depth and specificity. Future research should improve LLM performance by integrating specialized databases and expert knowledge to support patient-centred care.<br />Competing Interests: The authors declare no conflicts of interest.<br /> (© 2023 The Authors. Skin Health and Disease published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.)

Details

Language :
English
ISSN :
2690-442X
Volume :
4
Issue :
1
Database :
MEDLINE
Journal :
Skin health and disease
Publication Type :
Academic Journal
Accession number :
38312244
Full Text :
https://doi.org/10.1002/ski2.313