Patient education tools for chronic pain medications: Artificial intelligence chatbot versus traditional patient information leaflets

Dear Editor, We read an interesting article about using artificial intelligence (AI) chatbots for patient information leaflets (PILs).[1] The purpose of this study was to compare the performance of ChatGPT, an AI language model, in creating learning materials about chronic pain drugs to standard PILs. A significant limitation of this study is its dependence on a limited sample of questions extracted from PILs, which may not capture all patient queries or concerns. Furthermore, the emotional tone evaluation utilised may not accurately reflect the complexities of patients’ experiences with chronic pain treatments. The expert panel’s assessment involves subjective bias, which may impair the credibility of the conclusions. Furthermore, the emphasis on quantitative measurements of readability may neglect qualitative components of user experience, which are critical for patient education. Future steps could involve larger studies covering a broader range of chronic pain-related issues, allowing for greater generalisability and expanding tone analysis parameters to incorporate feedback or patient interviews. This could help us better understand the efficacy of traditional and AI-generated media communications. Incorporating user-experience testing with a broad patient population could also help learn how different groups interpret AI-generated material versus conventional media. To add variety, researchers might investigate using adaptive AI systems capable of tailoring patient education to individual requirements and health literacy levels. Furthermore, examining the long-term effects of AI-generated media use on patient adherence, comprehension, and satisfaction could aid in determining the effectiveness of such tools in real-world situations. Finally, evaluating the ethical implications and consequences of misinformation in AI-generated material is critical as they can substantially impact patient outcomes and the trustworthiness of digital health information providers. Future research could look into integrating decision assistance tools into electronic medical records to see how new features can improve the usability of automated lists. Such toolkits could incorporate customised features particular to the inpatient program and workflow. Furthermore, research into machine learning algorithms that update lists based on previous patient interactions and outcomes could be a big step in improving the efficiency of inpatient list management. Finally, the performance of AI-generated vs traditional patient information may be systematically evaluated, as shown in Table 1.Table 1: Issues for comparison between performance of Artificial Intelligence generated vs traditional patient informationFinancial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.