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.

通过主题分析，我们研究了具有生成性视觉问答系统的患者面向的生成式人工智能系统，以满足癌症患者在放射学成像数据中对医学术语、疾病进展、诊断步骤和治疗方案等方面信息的需求，结果表明模型的响应质量在不同主题上有所变化，强调患者面向的生成式人工智能系统需要满足患者的多样化实际需求。