Toward responsible AI governance: Balancing multi-stakeholder perspectives on AI in healthcare
Bibliographic record
Abstract
INTRODUCTION: The rapid integration of artificial intelligence (AI) into healthcare presents significant governance challenges, requiring balanced approaches that safeguard safety, efficacy, equity, and trust (SEET). This study proposes a cognitive framework to guide AI governance, addressing tradeoffs between speed, scope, and capability. OBJECTIVE: To develop a structured governance model that harmonizes stakeholder perspectives, focusing on multi-dimensional challenges and ethical principles essential for AI in healthcare. METHODS: A multidisciplinary team convened at the Blueprints for Trust conference, organized by the American Medical Informatics Association (AMIA), and the Division of Clinical Informatics at Beth Israel Deaconess Medical Center. Following extensive discussions with 190 participants across sectors, three governance models were identified to address specific domains: (1) Clinical Decision Support (CDS), (2) Real-World Evidence (RWE), (3) Consumer Health (CH). RESULTS: Three governance models emerged, tailored to CDS, RWE, and CH domains. Key recommendations include establishing a Health AI Consumer Consortium for patient-centered oversight, initiating voluntary accreditation and certification frameworks, and piloting risk-level-based standards. These models balance rapid adaptation with SEET-focused safeguards through transparency, inclusivity, and ongoing learning. CONCLUSION: A proactive, constraint-based governance framework is critical for responsible AI integration in healthcare. This structured, multi-stakeholder approach provides a roadmap for ethical, transparent governance that can evolve with technological advancements, enhancing trust and safety in healthcare AI applications.
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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 | 0.103 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.012 | 0.012 |
| 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".