Human-AI Medical Decision-Making Under Uncertainty
Bibliographic record
Abstract
AI-based decision support systems are becoming increasingly common in healthcare, promising improved accuracy and efficiency in diagnosis, treatment, and prediction. At the same time, due to the high-stakes and highly regulated context of medical decisions, uncertainty is neither an exception, nor a mere individual decision characteristic. Rather, uncertainty is a continuously constructed and enacted phenomenon by both focal medical professionals and the array of actors and technologies involved in healthcare tasks. The purpose of this panel symposium is to engage a group of panelists in a formal, moderated, interactive discussion on how uncertainty is dealt with by (a) medical doctors and healthcare professionals, (b) AI systems, and (c) human-AI ensembles. We would like to stimulate discussions around the changing nature of medical decision-making revolving around the new types of uncertainty emerging in the interactions between human and AI technologies. We would also like to examine the consequences of such interactions for the way medical decision making is enacted and organized.
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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 | 0.047 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".