Jul 2026· Medical Review· 0 citations· 94 references
TL;DR
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.
Abstract
Abstract Artificial intelligence-based software as a medical device (AI-SaMD) is increasingly used for screening, diagnosis, triage, risk stratification, treatment support, and monitoring. Its regulatory challenge lies not only in algorithmic complexity or software modification, but also in the possibility that safety and effectiveness may be affected after market entry by changing data distributions, model behavior, clinical workflows, user interaction, cybersecurity conditions, and software or model updates. This structured narrative review examines how AI-SaMD regulation is moving beyond single-point premarket evaluation toward continuous and dynamic oversight. We analyze how the international medical device regulators forum (IMDRF) clinical evaluation components, including valid clinical association, analytical validation, and clinical validation, can be operationalized across major AI-SaMD functions, and compare regulatory developments in the United States, European Union, China, Singapore, and the United Kingdom. Building on current evidence gaps, we synthesize 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 (RWD/RWE) with change management. Comparative analysis of Airdoc-AIFUNDUS, IDx-DR, and EyeArt illustrates how these dimensions operate in practice. Current governance still faces unresolved challenges in predetermined change control plan (PCCP) boundaries, RWE quality, post-update human factors reassessment, and cross-jurisdictional divergence.
Artificial intelligence (AI)-enabled medical device software is increasingly expected to learn, update, explain, retrieve information, draft records, and support clinical reasoning across changing care environments. Regulatory science has responded with lifecycle-oriented tools, including software-as-a-medical-device risk categorization, quality management systems, medical device software lifecycle processes, risk management, post-market monitoring, and predetermined change control plans. These tools are necessary, but they do not by themselves specify who must respond when field experience shows that an AI-supported clinical decision, workflow, validation claim, or planned update no longer fits clinical reality. Documented deployments of sepsis early-warning software in critical care—an external validation that overturned a widely deployed model’s performance claims, the manufacturer’s subsequent model replacement, and a prospective multi-site study linking alert response latency to sepsis mortality—show that such field signals are common, consequential, and unevenly answered. We propose lifecycle answerability as a regulatory science construct for AI-enabled medical device software. It specifies standing, addressee, reason-giving, temporal trigger, and revision pathway. We provisionally define the credible field signal that triggers these obligations, differentiate the construct from established algorithmic accountability frameworks, apply it in parallel to sepsis early warning and large language model documentation, and examine what is distinctive about answerability obligations in critically ill populations. Future work should test answerability through deployment case reviews, post-market signal audits, escalation pathway simulations, and implementation studies. Lifecycle answerability complements existing lifecycle governance by specifying the institutional response architecture through which credible field signals become institutionally actionable across the software lifecycle.
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.
Sebastian Schleidgen, O. Friedrich· European journal of internal...· 0 citations
Healthcare systems are rapidly embedding adaptive and generative AI into core clinical processes. The integration of Artificial Intelligence into Clinical Decision Support Systems (AI-CDSS) highlights a fundamental transformation within healthcare delivery. This transformation enables advanced predictive analytics, multimodal data integration, and real-time augmentation of clinical decisions. However, AI introduces systemic, ethical, operational, and governance risks that challenge traditional healthcare audit and assurance frameworks. In fact, assurance methodologies designed for static software are becoming insufficient for patient safety and regulatory compliance. This review discusses the evolving landscape of AI-CDSS audit, highlighting its transition from a technically focused lifecycle validation to an integrated paradigm centered on socio-technical resilience. Early audit approaches adapted conventional medical device and software validation models to machine learning–enabled clinical tools. While these models established baseline safety oversight, they were not designed to address adaptive algorithms functioning in complex, evolving clinical environments. Modern governance frameworks emphasize continuous performance monitoring, lifecycle surveillance, and structured human oversight. However, a persistent implementation gap remains. Many audit models poorly capture the interactions among algorithmic behavior, clinical workflows, organizational culture, and shifting patient populations. In fact, three major paradigm shifts are reforming the AI-CDSS audit. First, the audit scope is expanding from model-centric evaluation to ecosystem-level assurance that incorporates workflow integration, human-machine collaboration quality, and organizational learning capacity. Second, the field is moving from post-hoc explainability toward reasoning traceability and synergistic clinical sense-making. Third, audit philosophy is shifting from static compliance verification toward resilience-oriented monitoring that focuses on adaptive capacity, graceful degradation, and safe performance evolution. Thus, we propose the STRAICS framework (Socio-Technical Resilience Assurance for Intelligent Clinical Systems), which integrates technical robustness, human-machine interaction safeguards, adaptive governance, and transparency-by-design infrastructure. These components are vital for building trustworthy, effective, and impartial clinical AI ecosystems that can safely manage the increasing complexity of healthcare.
Rami A. Al‐Horani, Amanuel F. Tadesse· Frontiers in Artificial Inte...· 0 citations
Artificial intelligence (AI) is transforming the field of clinical medicine at a fast rate. AI is being used in the field of clinical medicine in areas such as diagnostic imaging, prediction, documentation, and large language model-based clinical reasoning tools such as ChatGPT. Algorithm-based triage tools are also being used in clinical settings such as hospitals. Radiology departments are also using AI-based detection tools. However, the rate at which AI is transforming the field of clinical medicine is much higher than the rate at which the regulatory bodies are keeping pace with the developments in the field. Even though the Food and Drug Administration (FDA) has developed a pathway to regulate AI-based Software as a Medical Device (SaMD) and machine learning-based technologies, the current system that is in place to ensure the safety and efficacy of these technologies was developed to ensure the safety and efficacy of static technologies and not those that have the capability to learn. The rate at which AI is being incorporated in the field of clinical medicine is much higher than the rate at which these technologies are being regulated. The issue of clinical AI governance is not about the regulation of AI; it is about patient safety. This article outlines the need for a structured framework of five principles of safety, accountability, fairness, explainability, and adaptive oversight.
N. Butt· Pioneer Journal of Biostatis...· 0 citations
Artificial intelligence (AI) has transformed the modern medical landscape. Medical devices benefit from innovative data collection, utilization of machine learning, predictive analytics, and intelligent decision support systems in their design and implementation. Applications include medical devices for diagnosis, screening, patient monitoring, tailored care, robotic procedures, and streamlining hospital processes. Despite these benefits, the development of new AI-powered devices presents many new regulatory, legal, ethical and cyber security challenges to be addressed. This research paper provides a review of the recent literature regarding advances in AI-powered medical devices, including their mechanisms of action, clinical applications, regulation, and regulation challenges. A qualitative narrative review was performed synthesizing the content of 15 carefully selected papers that fit the inclusion criteria. An analysis revealed that AI-powered medical devices have improved accuracy in diagnosis, enabled precision care, advanced patient monitoring, and optimized component manufacturing. Our review further identified a future where evolving regulation frameworks, such as risk-based control strategies, as well as increased explainability, interpretability, infrastructure protection, and compliance will help to facilitate widespread AI adoption in medicine.
C. Liakou, Marios Papadakis, Markos Plytas· International Journal of Med...· 0 citations
Abstract Laboratory medicine is undergoing a profound transformation driven by advances in artificial intelligence (AI), automation, and data interoperability. By 2050, laboratories may evolve from analytical testing facilities into an interconnected health intelligence capable of translating biological, digital, and environmental data into more personalized, preventive, and sustainable healthcare solutions. Building on previous foresight analysis conducted by the IFCC Emerging Technologies Division (ETD), global healthcare outlook reports, and expert-informed horizon scanning, this paper explores ten megatrends that may shape laboratory medicine by mid-century. These include precision multi-omics, AI-supported diagnostics, distributed healthcare models, patient-owned data ecosystems, digital twins, convergence of imaging and laboratory medicine, population-wide prevention strategies, regenerative therapies, sustainability imperatives, and workforce transformation. Collectively, these developments represent a paradigm shift from data generation to data interpretation, positioning laboratories as trusted nodes within the health intelligence network connecting patients, clinicians, and healthcare systems. The integration of automation with advanced multi-omics technologies, including robotic liquid handling, autonomous LC-MS/MS platforms, spatial proteomics, and real-time metabolomics, will accelerate the transition toward precision and planetary health. Four exploratory scenarios illustrate plausible trajectories for laboratory medicine in 2050. The Hyper-Intelligent Laboratory explores the implications of self-learning systems and advanced analytics capable of supporting earlier disease prediction and intervention. The Sustainable and Regenerative Laboratory illustrates how environmental stewardship and diagnostic excellence may converge within circular healthcare ecosystems. The Patient-in-the-Loop Revolution examines a future in which citizens become active stewards of their health data and participants in healthcare decision-making. The Spacefaring Laboratory extends this reflection beyond Earth, illustrating how extreme environments may accelerate innovation in autonomous diagnostics, sustainability, and human health monitoring. By 2050, laboratory medicine could serve as a central intelligence layer within healthcare systems, transforming biological signals into actionable knowledge. Achieving this vision will require scientific innovation, regulatory adaptability, equitable access, ethical governance, sustainability, and a future-ready workforce.
Damien Gruson, Bernard Gouget, Woochang Lee et al.· Clinical Chemistry and Labor...· 0 citations
