Patients’ attitudes toward artificial intelligence across the cancer care continuum: A mixed-methods study.

Abstract

428 Background: Artificial intelligence (AI) is increasingly incorporated into healthcare, yet patient perspectives on AI in oncology remain underexplored. Cancer presents an important clinical context, characterized by high stakes, uncertainty, and longitudinal clinician-patient relationships. Understanding patient perspectives is essential to inform effective, trustworthy AI implementation in oncology. Methods: In this mixed-methods study at a cancer center in New York City, survey questions first captured patients’ fixed-choice responses (very/somewhat positive or negative, unsure) to 5 use-cases involving AI in: diagnostic/screening imaging, treatment planning, triage of patient portal messages about symptoms, survivorship risk assessment, and clinical trial matching. Group discussion then delved deeply into participants’ views. Sessions were audio-recorded, transcribed, and analyzed to explore context-dependent attitudes and other factors shaping acceptability. Results: Attitudes varied substantially by use-case. While AI-assisted imaging was seen as providing quality assurance, AI triage of patient-reported symptoms elicited concerns about missed urgency, delayed care, and loss of human contact. Treatment recommendations and survivorship risk assessment use-cases highlighted the importance of human interpretation and the patient-physician relationship. AI-assisted clinical trial matching was viewed positively, though some participants raised concerns about accuracy and bias. Across use-cases, participants articulated conditions for acceptable AI use: clinician oversight, transparency, and equity. Conclusions: Patients’ views of AI in oncology are nuanced and context-dependent. While many see promise in AI across the care continuum, acceptance is contingent on preserving clinician involvement and attending to concerns of depersonalization, errors, and inequity. These novel insights can guide additional research and inform effective, trustworthy AI implementation in oncology.