Are Generative AI systems Capable of Supporting Information Needs of Patients?

• URL: http://arxiv.org/abs/2402.00234v1
• Date: Wed, 31 Jan 2024 23:24:37 GMT
• Title: Are Generative AI systems Capable of Supporting Information Needs of Patients?
• Authors: Shreya Rajagopal, Subhashis Hazarika, Sookyung Kim, Yan-ming Chiou, Jae Ho Sohn, Hari Subramonyam, Shiwali Mohan
• Abstract summary: We investigate whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data. We conducted a formative need-finding study in which participants discussed chest computed tomography (CT) scans and associated radiology reports of a fictitious close relative with a cardiothoracic radiologist. Using thematic analysis of the conversation between participants and medical experts, we identified commonly occurring themes across interactions. We evaluate two state-of-the-art generative visual language models against the radiologist's responses.
• Score: 4.485098382568721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Patients managing a complex illness such as cancer face a complex information challenge where they not only must learn about their illness but also how to manage it. Close interaction with healthcare experts (radiologists, oncologists) can improve patient learning and thereby, their disease outcome. However, this approach is resource intensive and takes expert time away from other critical tasks. Given the recent advancements in Generative AI models aimed at improving the healthcare system, our work investigates whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data. We conducted a formative need-finding study in which participants discussed chest computed tomography (CT) scans and associated radiology reports of a fictitious close relative with a cardiothoracic radiologist. Using thematic analysis of the conversation between participants and medical experts, we identified commonly occurring themes across interactions, including clarifying medical terminology, locating the problems mentioned in the report in the scanned image, understanding disease prognosis, discussing the next diagnostic steps, and comparing treatment options. Based on these themes, we evaluated two state-of-the-art generative visual language models against the radiologist's responses. Our results reveal variability in the quality of responses generated by the models across various themes. We highlight the importance of patient-facing generative AI systems to accommodate a diverse range of conversational themes, catering to the real-world informational needs of patients.

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