Patient Attitudes Toward Artificial Intelligence in Cancer Care: Scoping Review.

Abstract

Background: Artificial intelligence is reshaping cancer care, but little is known about how people with cancer perceive its integration into their care. Understanding these perspectives is essential to ensuring artificial intelligence adoption aligns with patient needs and preferences while supporting a patient-centered approach.

Objective: The aim of this study is to synthesize existing literature on patient attitudes toward artificial intelligence in cancer care and identify knowledge gaps that can inform future research and clinical implementation.

Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. MEDLINE, Embase, PsycINFO, and CINAHL were searched for peer-reviewed primary research studies published until February 1, 2025. The Population-Concept-Context framework guided study selection, focusing on adult patients with cancer and their attitudes toward artificial intelligence. Studies with quantitative or qualitative data were included. Two independent reviewers screened studies, with a third resolving disagreements. Data were synthesized into tabular and narrative summaries.

Results: Our search yielded 1240 citations, of which 19 studies met the inclusion criteria, representing 2114 patients with cancer across 15 countries. Most studies used quantitative methods (9/19, 47%) such as questionnaires or surveys. The most studied cancers were melanoma (375/2114, 17.7%), prostate (n=323, 15.3%), breast (n=263, 12.4%), and colorectal cancer (n=251, 11.9%). Although patients with cancer generally supported artificial intelligence when used as a physician-guided tool (9/19, 47%), concerns about depersonalization, treatment bias, and data security highlighted challenges in implementation. Trust in artificial intelligence (10/19, 53%) was shaped by physician endorsement and patient familiarity, with greater trust when artificial intelligence was physician-guided. Geographic differences were observed, with greater artificial intelligence acceptance in Asia, while skepticism was more prevalent in North America and Europe. Additionally, patients with metastatic cancer (99/2114, 5%) were underrepresented, limiting insights into artificial intelligence perceptions in this population.

Conclusions: This scoping review provides the first synthesis of patient attitudes toward artificial intelligence across all cancer types and highlights concerns unique to patients with cancer. Clinicians can use these findings to enhance patient acceptance of artificial intelligence by positioning it as a physician-guided tool and ensuring its integration aligns with patient values and expectations.