HEALTH INFORMATICS: AI in health care: a tool for physician leaders
Bibliographic record
Abstract
Artificial intelligence (AI) in health care is rapidly expanding, with the daily emergence of new initiatives, topics, and critical issues, making it challenging for physician leaders to organize and distill this complex topic. We offer a simple approach that involves classifying topics by three levels of scale: the individual, the organization, and the system or sector. Despite the widespread adoption of AI applications across all aspects of our daily lives, its implementation in health care remains limited. There is a need to engage, in all stages of development, key stakeholders, specifically governments, technology companies, health care providers, patients, and civil society. Cultural, social, and/or regional disparities can impact the integration of AI in health care, reflecting varied beliefs, attitudes, and practices. Our simplified approach to structuring and organizing this complex subject can serve as a valuable tool for physician leaders in conducting more focused discussions with stakeholders and decision-makers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".