A survey of consumer health question answering systems

Abstract

Consumers are increasingly using the web to find answers to their health‐related queries. Unfortunately, they often struggle with formulating the questions, further compounded by the burden of having to traverse long documents returned by the search engine to look for reliable answers. To ease these burdens for users, automated consumer health question answering systems try to simulate a human professional by refining the queries and giving the most pertinent answers. This article surveys state‐of‐the‐art approaches, resources, and evaluation methods used for automatic consumer health question answering. We summarize the main achievements in the research community and industry, discuss their strengths and limitations, and finally come up with recommendations to further improve these systems in terms of quality, engagement, and human‐likeness.

Developing a Medical Chatbot: Integrating Medical Knowledge into GPT for Healthcare Applications

“…While several other AI applications such as question answering methods provided reasonable information [9], GPT-4 seemingly outperforms competition particular in its generality and generativity [8,10,11]. AI vs MD [2]: ChatGPT Outperforms Physicians in Providing High-Quality, Empathetic Healthcare Advice -SciTechDaily: This article reports on a study that compared the quality and empathy of the responses of ChatGPT and physicians to real-world health questions.…”