Perceptions, Attitudes, and Concerns on Artificial Intelligence Applications in Patients with Cancer
Bibliographic record
Abstract
Introduction The use of artificial intelligence (AI) in oncology has increased rapidly, transforming various healthcare areas such as pathology, radiology, diagnostics, prognosis, genomics, treatment planning, and clinical trials. However, perspectives, comfort levels, and concerns about AI in cancer care remain largely unexplored. Materials and Methods This prospective, descriptive cross-sectional survey study was conducted between May 20, 2024 and October 22, 2024, among 363 patients with cancer from two different hospitals affiliated with Ankara University, a tertiary care center in Türkiye. The survey included three distinct sections: (1) Perceptions: Patients’ general views on AI’s impact in oncology; (2) Attitudes: Comfort level with AI performing medical tasks; (3) Concerns: Specific fears related to AI implementation (eg, diagnostic errors, data privacy, healthcare costs). Survey responses were summarized descriptively, and differences by age, gender, and education were analyzed using chi-square tests. Results A majority (50.7%) believed AI would somewhat (32%) or significantly (18.7%) improve healthcare. However, one-third of patients (33.1%) were very uncomfortable with AI diagnosing cancer, with higher discomfort among less-educated participants ( P < .005). Top patient concerns included AI making incorrect diagnoses (31.1%), increasing healthcare costs (27.5%), and not keeping data private (19.6%). Patients with higher education levels expressed less discomfort and fewer concerns. Conclusions Patients’ perceptions and attitudes on AI varied significantly based on education, highlighting the need for targeted educational initiatives. While AI holds potential to revolutionize cancer care, addressing concerns about accuracy, security, and transparency is critical to enhance its acceptance and effectiveness in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".