Complexities and Questions Toward Artificial Intelligence for Diagnostic Support in Virtual Primary Care
Bibliographic record
Abstract
The challenge of effectively deploying artificial intelligence (AI) technologies into real-world settings is understudied for primary care, which has received less focus than other health care sectors in the AI revolution.1 Zeltzer et al,2 address this gap with their study published in Mayo Clinic Proceedings: Digital Health on 102,059 AI-generated diagnoses in virtual primary care encounters through the K Health platform in the United States from October 2022 to January 2023.2 Their research suggests potential roles for AI to support virtual primary care, and showcases diagnostic challenges and sociotechnical complexities amidst the broad scope of primary care.
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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 | 0.066 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.020 | 0.040 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".