Investigating the comparative superiority of artificial intelligence programs in assessing knowledge levels regarding ocular inflammation, uvea diseases, and treatment modalities.

IF 1 Q4 OPHTHALMOLOGY Taiwan Journal of Ophthalmology Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI:10.4103/tjo.TJO-D-23-00166
Eyupcan Sensoy, Mehmet Citirik
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Abstract

Purpose: The purpose of the study was to evaluate the knowledge level of the Chat Generative Pretrained Transformer (ChatGPT), Bard, and Bing artificial intelligence (AI) chatbots regarding ocular inflammation, uveal diseases, and treatment modalities, and to investigate their relative performance compared to one another.

Materials and methods: Thirty-six questions related to ocular inflammation, uveal diseases, and treatment modalities were posed to the ChatGPT, Bard, and Bing AI chatbots, and both correct and incorrect responses were recorded. The accuracy rates were compared using the Chi-squared test.

Results: The ChatGPT provided correct answers to 52.8% of the questions, while Bard answered 38.9% correctly, and Bing answered 44.4% correctly. All three AI programs provided identical responses to 20 (55.6%) of the questions, with 45% of these responses being correct and 55% incorrect. No significant difference was observed between the correct and incorrect responses from the three AI chatbots (P = 0.654).

Conclusion: AI chatbots should be developed to provide widespread access to accurate information about ocular inflammation, uveal diseases, and treatment modalities. Future research could explore ways to enhance the performance of these chatbots.

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研究人工智能程序在评估眼部炎症、葡萄膜疾病和治疗方法相关知识水平方面的比较优势。
目的:本研究的目的是评估 Chat Generative Pretrained Transformer(ChatGPT)、Bard 和 Bing 人工智能(AI)聊天机器人对眼部炎症、葡萄膜疾病和治疗方式的了解程度,并研究它们之间的相对性能比较:向 ChatGPT、Bard 和 Bing 人工智能聊天机器人提出了 36 个与眼部炎症、葡萄膜疾病和治疗方式有关的问题,并记录了正确和错误的回答。使用卡方检验比较了正确率:结果:ChatGPT 提供了 52.8% 的正确答案,Bard 回答了 38.9% 的正确答案,Bing 回答了 44.4% 的正确答案。所有三个人工智能程序都对 20 个问题(55.6%)做出了相同的回答,其中 45% 回答正确,55% 回答错误。三个人工智能聊天机器人的正确回答和错误回答之间没有明显差异(P = 0.654):结论:应开发人工智能聊天机器人,以广泛提供有关眼部炎症、葡萄膜疾病和治疗方法的准确信息。未来的研究可以探索提高这些聊天机器人性能的方法。
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来源期刊
CiteScore
1.80
自引率
9.10%
发文量
68
审稿时长
19 weeks
期刊最新文献
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