Bibliographic record
Abstract
Balancing automation and accountability is fundamental in any healthcare field, particularly under mandates from the world's first AI act. Yet, the act relies on self-assessment. Here, we draw from a half century of theoretical cognitive neuroscience theories and analyze emerging computer science principles to develop an actionable blueprint to advance beyond self-assessment protocols for responsible Human-Clinical AI Collaboration. Our framework proactively identifies and mitigates risk through four key contributions: (1) interactive healthcare simulations populated by Clinical AI Agents as experimental testbeds to systematically evaluate human-AI collaboration without exposing patients to harm; (2) cognitive-state aware AI that adapts its behaviour based on measured physiological signals indicating cognitive load; and (3) critical safety mechanisms that enable Clinical AI Agents to disengage when detecting insufficient clinician engagement, preventing dangerous over-reliance; (4) emphasizing interpretable models for high-risk decisions and physiologically-adaptive explanations. These innovations address the fundamental mismatch between the dynamic nature of human cognition and the static interaction patterns of current Clinical AI systems, anticipating and mitigating both dangerous over-reliance and disengagement from algorithmic insights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".