Patient Perspectives on AI-Powered Medical Robots in Breast and Prostate Cancer Care: Qualitative Study
Bibliographic record
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. Artificial intelligence (AI) applications can potentially improve the accuracy, efficiency, and timeliness of cancer care, including screening, diagnostic imaging, and early treatment. However, patient acceptability of AI in cancer care remains underexplored. Objective: This study aimed to understand the feelings and perceptions of and acceptability to patients with breast and prostate cancer regarding the inclusion of AI-powered medical robots for cancer screening, diagnosis, and early treatment and to identify barriers and facilitators to implementation. Methods: In this qualitative study, semistructured interviews were conducted with 15 patients with breast (n=6) or prostate (n=9) cancer. Participants (mean [SD] age 67 [12] y; range 41-88 y) were recruited from Sunnybrook Health Sciences Centre between May and November 2022. Each participant completed 2 semistructured interviews, each approximately 1 hour in length, conducted by telephone or Zoom by a research assistant. The first interview explored initial reactions and expectations regarding AI-assisted medical technologies, allowing us to tailor questions for the second interview to better understand practical means of introducing AI into care, while further exploring salient concepts. Data were analyzed using a conventional content analysis approach. Two research assistants independently and inductively coded transcripts, iteratively generating and refining a codebook. Data saturation was assessed after 10 interviews and confirmed through analysis of all 15 participants. Results: Three overarching categories were developed: (1) individual beliefs, understanding, and attitudes; (2) integration of AI into care; and (3) health structure, systems, and processes. Participants reported openness toward AI-assisted medical robots but emphasized the importance of reduced wait times, demonstrated safety and reliability, and patient-centered care. Patients indicated that with appropriate education and transparent communication, they would be willing to accept AI-assisted care due to its enhanced accuracy and efficiency. Key barriers included concerns about reliability, potential loss of human interaction, and inadequate mitigation strategies for technical failures. Facilitators included timely results, improved efficiency, accessible educational resources, and assurance that AI would complement rather than replace human expertise. Conclusions: Participants demonstrated cautious willingness to accept AI-powered medical robots in cancer care if positioned as complementary to, rather than substitutes for, human-provided care. These findings underscore the need for maintaining the presence of health care providers during AI-assisted procedures, providing clear and accessible education, and ensuring transparent communication about safety and reliability. Personalizing patient education and offering multiple modes of information delivery may foster confidence and improve acceptability. While findings are exploratory and reflect the perspectives of a small, predominantly urban sample, they provide actionable insights into patient concerns and priorities that may inform future research and guide early implementation strategies in integrating AI into cancer care pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".