Continuous assurance for AI-driven clinical decision support systems

Clinical MLOps: A Framework for Responsible Deployment and Observability of AI Systems in Cloud-Native Healthcare Platforms

Machine Learning Operations (MLOps) practices have reached a notable level of maturity within general-purpose software engineering. Pipelines are standardized, monitoring is automated and deployment patterns are well rehearsed. Yet when these same practices are transferred into clinical environments, their adequacy becomes less certain. The healthcare domain introduces operational and ethical pressures that conventional MLOps frameworks were not originally designed to address. Clinical AI systems operate under constraints that extend beyond typical production settings. Stringent data protection regimes including GDPR and the EU AI Act shape how data can be processed and retained. Model drift is not merely a statistical inconvenience; it may alter clinical decisions with tangible consequences for patient outcomes. Regulatory compliance demands comprehensive, tamper-evident audit trails. At the same time, decision-critical contexts impose a clear expectation of human oversight. In other words, technical robustness alone is insufficient; governance and accountability become integral system properties.

Artificial intelligence in medicine: An ethical and legal review.

This review addresses the primary ethical principles relevant to AI in medicine - including respect for patient autonomy, beneficence, non-maleficence, and justice - alongside key legal frameworks with respect to liability, data protection, regulatory compliance, and algorithmic transparency.

Artificial intelligence-based software as a medical device: regulatory evolution toward continuous oversight

This structured narrative review examines how AI-SaMD regulation is moving beyond single-point premarket evaluation toward continuous and dynamic oversight, and synthesizes five evidence dimensions central to ongoing regulatory assurance: data evidence, algorithm evidence, software and cybersecurity evidence, clinical scenario and human factors evidence, and real-world data/real-world evidence with change management.

[Artificial intelligence and healthcare: why is it difficult to move from the research phase to clinical practice?]

Artificial intelligence (AI) is widely regarded as one of the most promising innovations in healthcare, yet its adoption in routine clinical practice remains limited. Only a small proportion of AI applications developed in research settings are successfully integrated into healthcare delivery. Major barriers include poor interoperability with existing health information systems, complex regulatory requirements, limited scientific evidence, and the lack of clear clinical guidelines. Many AI tools have been evaluated through methodologically weak studies, often retrospective and lacking external validation, contributing to skepticism among healthcare professionals. Additional challenges involve healthcare professionals' education and training, algorithm transparency, and the ability of healthcare organizations to effectively incorporate these technologies into clinical workflows. To promote the safe and effective adoption of AI, stronger clinical evidence, structured training programs, and organizational models capable of supporting its implementation are required. Addressing these issues is essential to ensure that AI can deliver meaningful benefits for patients, healthcare professionals, and healthcare systems.

Human-centered AI in healthcare teams: integrating clinical oversight, EHR auditability, and reimbursement pathways for responsible adoption

Background AI is being introduced into clinical workforces during a critical transition toward integrated, value-based models of care, where its greatest promise lies in augmenting clinician judgment and expanding the reach of already strained healthcare teams. Yet clinical adoption remains limited because most AI systems lack reimbursement pathways, impose substantial implementation costs, and lack standardized mechanisms for integration into electronic health records (EHRs). These gaps create misalignment between technological capability and clinical usability. This paper identifies financial, regulatory, and workflow structures required for AI to operate safely, predictably, and sustainably across key domains of healthcare. Methods This narrative synthesis reviews clinical, economic, regulatory, and implementation-science literature from 2022 to 2025. Four domains were analyzed: (1) AI augmentation of clinical workflows; (2) reimbursement structures and CPT coding pathways; (3) EHR-based AI deployment and governance; and (4) economic and equity considerations for large-scale implementation. Sources included peer-reviewed reviews, white papers, consensus statements, and health policy analyses. Results AI tools demonstrated benefits in diagnostic accuracy, decision support, and documentation efficiency, particularly in radiology, cardiology, and EHR-integrated workflows. Adoption was hindered by absent reimbursement for clinician-reviewed AI outputs. Implementation and monitoring costs fell heavily on health systems, risking widened disparities. Additional concerns included accuracy, bias, generalizability, and limited oversight. Enabling conditions included clinician-in-the-loop review, auditable outputs, equity-centered validation, and alignment with evolving payment models. Conclusions AI can strengthen healthcare teams and advance value-based care, but scaling requires aligned incentives, rigorous evaluation standards, equity safeguards, and coordinated governance—echoing lessons from national EHR implementation.

Ask-Audit-Apply: A Framework for Clinical Reasoning with Artificial Intelligence.