Why the study?
Heart failure patients may hesitate to ask questions or encounter delays getting responses from healthcare providers, prompting investigation into chat-based artificial intelligence platforms to complement patient-centered care.
How accurate and consistent are AI chatbots (ChatGPT-3.5 and Bard) in answering patient questions about heart failure?
How accurate and consistent are AI chatbots (ChatGPT-3.5 and Bard) in answering patient questions about heart failure?
AI chatbots like ChatGPT show high accuracy in answering patient questions about heart failure, but issues with hallucination and outdated recommendations remain.
Background Heart failure (HF) is a prevalent condition associated with significant morbidity. Patients may have questions that they feel embarrassed to ask or will face delays awaiting responses from their healthcare providers which may impact their health behavior. We aimed to investigate the potential of chat-based artificial intelligence (AI) platforms in complementing the delivery of patient-centered care. Methods Using online patient forums and physician experience, we created 30 questions related to diagnosis, management and prognosis of HF. The questions were posed to two artificial intelligence (AI) chatbots (OpenAI’s ChatGPT-3.5 and Google’s Bard). Each set of answers was evaluated by two HF experts, independently and blinded to each other, for accuracy (adequacy of content) and consistency of content. Results ChatGPT provided mostly appropriate answers (27/30, 90%) and showed a high degree of consistency (93%). Bard provided a similar content in its answers and thus was evaluated only for adequacy (23/30, 77%). The two HF experts’ grades were concordant in 83% and 67% of the questions for ChatGPT and Bard, respectively. Both platforms suffered from issues related to “hallucination” of facts and/or difficulty with more contemporary recommendations. Conclusion AI based chatbots may have potential in improving HF education and empowering patients, but their limitations should be considered and addressed in future research.
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Kozaily et al. (2023) studied this question.
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