JMIR Publications

ABSTRACT

Background:

Nearly 250,000 cancer cases are diagnosed annually in Canada, with breast and prostate cancer representing 25% and 22% of new cases, respectively. Access to cancer care is often fraught with barriers and delays due to limited healthcare resources. AI applications in diagnostic imaging have enabled detection of early cancerous lesions with greater accuracy and efficiency. However, patient acceptability of AI in cancer care remains under-explored.

Objective:

The objective of this study was to understand breast and prostate cancer patients’ feelings, perceptions, and acceptability regarding the inclusion of AI-powered medical robots for cancer screening, diagnosis, and early treatment (Aim 1) and identifies barriers and facilitators to implementation (Aim 2).

Methods:

In this qualitative study, semi-structured interviews were conducted with 15 patients with breast or prostate cancer. Participants were recruited from the Odette Cancer Centre in Toronto over six months (between May and November 2022). Data were analyzed using a conventional content analysis.

Results:

Three categories emerged; Individual beliefs, understanding, and attitudes; Integration of AI into care; and Health structure, systems, and processes. Responses highlighted openness to AI-assisted medical robot integration in their cancer care, but with hesitancy. When considering introducing AI into their care, participants described the importance of reduced wait-times, benefits to care, extensive research on safety and reliability, and maintaining patient-centred care. Importantly, patients indicated that with appropriate education, clear communication about the technology, and assurances that AI would not diminish human interaction or judgment, they may accept AI-assisted care due to its enhanced accuracy and efficiency. Barriers included concerns about the reliability of AI-powered systems, potential loss of human interaction, and inadequate mitigation strategies for technical failures. Participants underscored the need for the continued presence of healthcare professionals during AI-assisted procedures to ensure safety and support.

Conclusions:

Patients demonstrated a willingness to accept AI-powered medical robots in cancer care if these technologies are positioned as complementary to human-provided services rather than replacements. As cancer care advances into an era further integrating AI-technology, implementation plans should focus on ensuring that the human element remains present through maintaining patient-care team interactions, patient-centred education, and transparent communication. Personalizing patient education can support patient-centred care. In addition, it is essential that patients are provided adequate and accessible educational resources and information to foster confidence in using AI medical robots in their cancer care. These findings provide actionable insights for integrating AI technology in oncology while safeguarding high-quality, patient-centered care.