Authors' Reply: Assessing the utility of <scp>ChatGPT</scp> as an artificial intelligence‐based large language model for information to answer questions on myopia

Biswas, Sayantan; Logan, Nicola S.; Davies, Leon N.; Sheppard, Amy L.; Wolffsohn, James S.

doi:10.1111/opo.13227

Ophthalmic Physiologic Optic

2023

DOI: 10.1111/opo.13227

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Authors' Reply: Assessing the utility of ChatGPT as an artificial intelligence‐based large language model for information to answer questions on myopia

Sayantan Biswas,

Nicola S. Logan,

Leon N. Davies

et al.

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Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration

Ferro Desideri,

Roth,

Zinkernagel

et al. 2023

Int J Retin Vitr

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Introduction Age-related macular degeneration (AMD) affects millions of people globally, leading to a surge in online research of putative diagnoses, causing potential misinformation and anxiety in patients and their parents. This study explores the efficacy of artificial intelligence-derived large language models (LLMs) like in addressing AMD patients' questions. Methods ChatGPT 3.5 (2023), Bing AI (2023), and Google Bard (2023) were adopted as LLMs. Patients’ questions were subdivided in two question categories, (a) general medical advice and (b) pre- and post-intravitreal injection advice and classified as (1) accurate and sufficient (2) partially accurate but sufficient and (3) inaccurate and not sufficient. Non-parametric test has been done to compare the means between the 3 LLMs scores and also an analysis of variance and reliability tests were performed among the 3 groups. Results In category a) of questions, the average score was 1.20 (± 0.41) with ChatGPT 3.5, 1.60 (± 0.63) with Bing AI and 1.60 (± 0.73) with Google Bard, showing no significant differences among the 3 groups (p = 0.129). The average score in category b was 1.07 (± 0.27) with ChatGPT 3.5, 1.69 (± 0.63) with Bing AI and 1.38 (± 0.63) with Google Bard, showing a significant difference among the 3 groups (p = 0.0042). Reliability statistics showed Chronbach’s α of 0.237 (range 0.448, 0.096–0.544). Conclusion ChatGPT 3.5 consistently offered the most accurate and satisfactory responses, particularly with technical queries. While LLMs displayed promise in providing precise information about AMD; however, further improvements are needed especially in more technical questions.

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Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration

Ferro Desideri,

Roth,

Zinkernagel

et al. 2023

Int J Retin Vitr

View full text Add to dashboard Cite

show abstract

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Authors' Reply: Assessing the utility of ChatGPT as an artificial intelligence‐based large language model for information to answer questions on myopia

Cited by 1 publication

References 11 publications

Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration

Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration

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