Patients' Views of Artificial Intelligence Across the Cancer Care Continuum: A Scenario-Based Attitudinal Assessment

Artificial intelligence (AI) is increasingly incorporated into health care, 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 human-centered AI implementation in oncology.

In this scenario-based attitudinal assessment at a cancer center in New York City, survey questions captured patients' fixed-choice responses (very/somewhat positive or negative, unsure) to five scenarios 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. Saturation was achieved with three sessions.

Attitudes of participants (N = 20: 14 female, 16 White, 18 non-Hispanic, average age 60 years) varied substantially by scenario. 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 planning and survivorship risk assessment scenarios highlighted the importance of human interpretation and the patient-physician relationship. AI-assisted clinical trial matching was viewed positively although some participants raised concerns about accuracy and bias. Across scenarios, participants articulated conditions for acceptable AI use, most notably clinician oversight, transparency, and equity.

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, human-centered AI implementation in oncology.