Attitudes and Perception of Artificial Intelligence in Hypertension: Cross-Sectional Survey Among Patients and Clinicians
Bibliographic record
Abstract
Background: Artificial intelligence clinical decision-support systems (AI-CDSS) show promise for improving blood pressure control, but limited data exist on clinicians' and patients' perceptions. This study explores both groups' views on the use of AI-CDSS in hypertension management. Methods: We conducted a cross-sectional survey (Aug 2024–Mar 2025) of primary and secondary care clinicians in Ireland, distributed through Nephrology, Hypertension, and General Practice Societies. A parallel patient survey was conducted via hypertension clinics and community networks. Surveys were designed using the Value-Based Adoption Model (perceived risks and benefits) and refined through a Public and Patient Involvement event. Responses were collected using a 5-point Likert scale. Results: 201 clinicians and 304 patients completed surveys. Most clinicians, 84 (42%) were aged 30–39 and most patients, 192 (64%) were ≥60 years. Basic AI understanding was reported by 164 clinicians (83%) and 206 patients (67%). Willingness to use AI-CDSS was high: 138 clinicians (71%) said they would use it, and 223 patients (75%) were comfortable with their doctor using it. The leading concern among clinicians was legal liability: 148 (77%) were worried about responsibility if AI caused harm. For patients, it was lack of regulation, cited by 172 (60%). The most unanimous response was to decision conflict: 290 patients (97%) said they would follow their doctor’s recommendation over AI’s. Conclusion: Both clinicians and patients are open to AI-CDSS use in hypertension. Patients emphasised the need for stronger regulation and clinicians highlighted liability concerns. Responses strongly affirm that AI must support, not replace clinician judgment. Funding: Government Support – Non-U.S.
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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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".