Symptom Checkers, Search Engines and Conversational Agents for Online Health Information Seeking

The School of EECS is hosting the following PhD Progress Review 3 Seminar:

Symptom Checkers, Search Engines and Conversational Agents for Online Health Information Seeking

Speaker: Sebastian Cross
Host: Prof Guido Zuccon

Abstract: In an increasingly digital world, searching for health advice online has become ubiquitous, leading to various tools designed to assist users in this quest. This thesis explores the comparative efficacy and influencing factors of three major platforms: Search Engines, Symptom Checkers, and Large Language Model (LLM)-powered Conversational Agents. The study focuses on Consumer Health Search (CHS), which involves seeking information to assist in self-diagnosis and triage. The first research question (RQ1) examines the distinctions between Search Engines and Symptom Checkers in their ability to support users seeking health advice, focusing on comprehensiveness, accuracy, and user experience. The second research question (RQ2) assesses the effectiveness of Search Engines versus LLM-powered Conversational Agents in delivering health-related information, considering reliability, clarity, and relevance. The third research question (RQ3) investigates the factors that influence the performance of LLM-powered Conversational Agents in the context of health advice, examining variables such as data quality, conversational interface design, user interaction patterns, and ethical implications of using AI in healthcare.

Bio: Sebastian Cross earned his Bachelor's degree in Information Technology from Queensland University of Technology in 2014, followed by an Honours degree in 2016. He is a member of IELAB at the University of Queensland, with expertise in online learning environments, learning analytics and online health seeking. Sebastian is pursuing a PhD, focusing on research in medical health-seeking systems, particularly those driven by conversational Agents.