AI Agents in Healthcare: The Need for Governance
Bibliographic record
Abstract
Artificial intelligence (AI) agents are poised to redefine virtual care by streamlining routine tasks and enhancing clinical decision-making. Unlike static decision aids, these systems can autonomously manage complex, multi-step processes such as care coordination, longitudinal patient monitoring, and data integration across fragmented health systems. Early applications include voice-enabled assistants for documentation and order entry, as well as health wallets that advance the self-sovereign patient paradigm by actively managing data sharing, consent, and treatment planning. The next stage is the rise of multi-agent systems, where specialized agents collaborate with one another and with clinicians to deliver distributed, adaptive care. These advances offer solutions to workforce shortages, administrative burden, and patient engagement, yet also raise new challenges around trust, liability, bias, and emergent risk. This article argues for governance by design as a critical framework, embedding oversight throughout the agent lifecycle and extending it from individual tools to collective multi-agent ecosystems.
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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 | 0.040 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".