相关论文: Regulating AI Adaptation: An Analysis of AI Medica…
Artificial intelligence (AI) is increasingly integrated into modern healthcare, offering powerful support for clinical decision-making. However, in real-world settings, AI systems may experience performance degradation over time, due to…
Observers and practitioners of artificial intelligence (AI) have proposed an FDA-style licensing regime for the most advanced AI models, or 'frontier' models. In this paper, we explore the applicability of approval regulation -- that is,…
This work addresses challenges in evaluating adaptive artificial intelligence (AI) models for medical devices, where iterative updates to both models and evaluation datasets complicate performance assessment. We introduce a novel approach…
Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such…
Prognostic and diagnostic AI-based medical devices hold immense promise for advancing healthcare, yet their rapid development has outpaced the establishment of appropriate validation methods. Existing approaches often fall short in…
Artificial intelligence (AI) systems are increasingly integrated into healthcare and pharmacy workflows, supporting tasks such as medication recommendations, dosage determination, and drug interaction detection. While these systems often…
Applications of artificial intelligence (AI) in drug development continue to increase at a rapid pace. Regulatory authorities have provided increasingly clear perspectives on the use of AI in regulated applications, including recent draft…
AI systems have found a wide range of application areas in financial services. Their involvement in broader and increasingly critical decisions has escalated the need for compliance and effective model governance. Current governance…
Over 1,200 AI-enabled medical devices have received marketing authorization from the U.S. FDA, yet identifying devices suited to specific clinical needs remains challenging because the FDA's databases contain only limited metadata and…
Artificial Intelligence and Machine Learning (AI/ML) models used in clinical settings are increasingly deployed to support clinical decision-making. However, when training data become stale due to changes in demographics, environment, or…
Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention within both the medical community and the regulators. In this study, we leverage a data-driven…
Medical Artificial Intelligence (AI) involves the application of machine learning algorithms to biomedical datasets in order to improve medical practices. Products incorporating medical AI require certification before deployment in most…
This article examines the evolving landscape of artificial intelligence (AI) regulation in financial services, detailing the legal frameworks and compliance challenges posed by rapid technological adoption. By reviewing current legislation,…
The era of AI regulation (AIR) is upon us. But AI systems, we argue, will not be able to comply with these regulations at the necessary speed and scale by continuing to rely on traditional, analogue methods of compliance. Instead, we posit…
Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how different regions approach AI development, balancing…
Fervent calls for more robust governance of the harms associated with artificial intelligence (AI) are leading to the adoption around the world of what regulatory scholars have called a management-based approach to regulation. Recent…
Foundation models - models trained on broad data that can be adapted to a wide range of downstream tasks - can pose significant risks, ranging from intimate image abuse, cyberattacks, to bioterrorism. To reduce these risks, policymakers are…
The automation of AI R&D (AIRDA) could have significant implications, but its extent and ultimate effects remain uncertain. We need empirical data to resolve these uncertainties, but existing data (primarily capability benchmarks) may not…
The increasing complexity of regulatory updates from global authorities presents significant challenges for medical device manufacturers, necessitating agile strategies to sustain compliance and maintain market access. Concurrently,…
The performance of AI models on safety benchmarks does not indicate their real-world performance after deployment. This opaqueness of AI models impedes existing regulatory frameworks constituted on benchmark performance, leaving them…