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Medication counseling for OTC drugs using customized ChatGPT-4: Comparison with ChatGPT-3.5 and ChatGPT-4o
4
Zitationen
3
Autoren
2025
Jahr
Abstract
Background: In Japan, consumers can purchase most over-the-counter (OTC) drugs without pharmacist guidance. Recently, generative artificial intelligence (AI) has become increasingly popular. Therefore, medical professionals need to consider the use of generative AI by consumers for medication counseling. We have previously reported responses in Japanese from ChatGPT-3.5 to 264 questions regarding whether each of 22 OTC drugs can be taken under 12 typical patient conditions. The proportion of responses that satisfied the criteria of 1) accuracy, 2) relevance, and 3) reliability with respect to package insert instructions was 20.8%. In November 2023, GPTs were launched, enabling us to construct a customized ChatGPT, using natural language. In the present study, we compared performance in providing medication guidance among a newly customized GPT, the latest non-customized version ChatGPT-4o, and the previous version, ChatGPT-3.5. The aim was to determine whether the customization and version update of ChatGPT improved performance and to evaluate its potential usefulness. Methods: We configured customized ChatGPT-4 by executing five instructions in Japanese and uploaded the text of package inserts for 22 OTC drugs as knowledge. We asked the same 264 questions as in our previous study. Results: With the customized ChatGPT-4, the percentages of responses that satisfied the criteria of accuracy, relevance, and reliability were 93.2%, 100%, and 60.2%, respectively. Additionally, 56.1% of responses satisfied all three criteria, 2.7-fold higher compared with ChatGPT-3.5 and 1.3-fold higher compared with ChatGPT-4o. Conclusion: The performance of our customized GPT far exceeded that of ChatGPT-3.5. In particular, the proportion of appropriate responses to the questions using brand names was significantly improved. ChatGPT can be customized by providing drug package insert information and using appropriate prompt engineering, potentially offering helpful tools in clinical pharmacy.
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