Trust in AI-Supported Screening in General Practice Among Urban and Rural Citizens: Cross-Sectional Study
Bibliographic record
Abstract
Background: The early detection of diseases is one of the tasks of general practice. Artificial intelligence (AI)-based technologies could be useful for identifying diseases at an early stage in general practices. As approximately 90% of the population regularly consults a general practitioner during one year, this could increase the percentage of citizens who take part in meaningful screening measures. Objective: This study aimed to evaluate the level of trust among citizens in rural and urban areas in AI-supported early detection measures in general practice. Methods: This cross-sectional study was conducted in the federal state of Schleswig-Holstein, Germany, from November 2023 to December 2023, on the topic of early detection measures with AI in general practice care, among other things. For this purpose, 5000 adult residents of rural areas (Ostholstein, Pinneberg, and Nordfriesland) and urban areas (the city of Kiel) were invited to take part in the survey. Data analysis was carried out using descriptive statistics, subgroup analysis, and linear and stepwise regression analysis to identify the factors that influenced trust in AI-based diagnoses. Results: Most respondents (787/1790, 44.0%) considered the introduction of an AI-based screening measure to be a sign of modern medicine. Moreover, 21.7% (n=388) of respondents feared that the introduction of such services could lead to a deterioration in the physician-patient relationship. The role of AI in future care was rated as very important by 35.4% (n=634) of respondents. The stepwise regression analysis showed that a positive attitude toward AI in medicine was the strongest predictor (ß=0.420) of trust in AI-based diagnoses. In contrast, trust in physician diagnoses was associated with lower age (ß=-0.111) and shorter waiting times for test results (ß=0.077). Conclusions: Trust in general practitioner-based diagnoses was approximately 6 times greater than trust in AI applications. Despite concerns about their impact on the physician-patient relationship, approximately one-third of participants believed that the role of AI in health care will grow.
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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 | 0.005 |
| 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) | 0.001 | 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".