From principles to practice: operationalizing responsible AI governance across predictive and generative clinical AI.
4 affiliations
- 1Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.
- 2Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan.
- 3Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan.
- 4General Education Center, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.
Abstract
Responsible artificial intelligence (AI) governance in healthcare is increasingly supported by international principles addressing transparency, accountability, safety, explainability, and lifecycle risk management. Yet a persistent implementation gap remains between these institution-level principles and the concrete controls encountered when an AI service is used in clinical practice. This Perspective argues that responsible AI should be operationalized at the functional points where clinical AI outputs are produced, explained, and communicated. We use three functional domains-Capability, Explainability, and Communication-as an organizing structure for translating existing governance principles into application-level specifications. The approach accommodates both predictive AI (PAI) and generative AI (GAI) while recognizing their different risk profiles. For PAI, governance emphasizes target and prediction-horizon definition, calibration, thresholds, drift, external validation, and alert burden. For GAI, it emphasizes task boundaries, source traceability, hallucination control, version management, human verification, and constraints on write-back or autonomous action. Two dialysis-related applications illustrate how the same governance vocabulary can be instantiated differently across AI modalities. Operationalizing responsible AI in this way does not introduce new governance principles; rather, it organizes existing requirements into observable, reviewable, and auditable controls embedded in clinical AI services and workflows.
Related
- Generative artificial intelligence in the preparation of scientific medical articles on surgery: principles of responsible use and current requirements of editorial organisations
- Responsible use of large language models in digital health: an equity-first governance framework
- Operationalizing the CBDR-RC principle in the WHO Pandemic Agreement: from environmental law to pandemic governance
- Balancing innovation and ethics: responsible governance of artificial intelligence hallucinations in healthcare.
- Governance Mechanisms for Responsible AI Implementation: Lessons from Nordic Health Systems
BibTeX
@article{liu2026from,
title = {{From principles to practice: operationalizing responsible AI governance across predictive and generative clinical AI.}},
author = {Chung-Feng Liu and Chia-Jung Chen and Yen-Ling Ko},
journal = {Frontiers in digital health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1965076},
url = {https://europepmc.org/article/MED/43011821},
note = {CC BY},
}
RIS
TY - JOUR
TI - From principles to practice: operationalizing responsible AI governance across predictive and generative clinical AI.
AU - Chung-Feng Liu
AU - Chia-Jung Chen
AU - Yen-Ling Ko
PY - 2026
DA - 2026/10/27
JO - Frontiers in digital health
DO - 10.3389/fdgth.2026.1965076
UR - https://europepmc.org/article/MED/43011821
AB - Responsible artificial intelligence (AI) governance in healthcare is increasingly supported by international principles addressing transparency, accountability, safety, explainability, and lifecycle risk management. Yet a persistent implementation gap remains between these institution-level principles and the concrete controls encountered when an AI service is used in clinical practice. This Perspective argues that responsible AI should be operationalized at the functional points where clinical AI outputs are produced, explained, and communicated. We use three functional domains-Capability, Explainability, and Communication-as an organizing structure for translating existing governance principles into application-level specifications. The approach accommodates both predictive AI (PAI) and generative AI (GAI) while recognizing their different risk profiles. For PAI, governance emphasizes target and prediction-horizon definition, calibration, thresholds, drift, external validation, and alert burden. For GAI, it emphasizes task boundaries, source traceability, hallucination control, version management, human verification, and constraints on write-back or autonomous action. Two dialysis-related applications illustrate how the same governance vocabulary can be instantiated differently across AI modalities. Operationalizing responsible AI in this way does not introduce new governance principles; rather, it organizes existing requirements into observable, reviewable, and auditable controls embedded in clinical AI services and workflows.
SN - 2673-253X
ER -