Goto

Collaborating Authors

Results


Abbott's imaging catheter recall hit with FDA's Class I designation – Fierce Biotech

#artificialintelligence

The more recent of these came in 2020 for a machine learning algorithm that can analyze non-contrast brain CT scans and pinpoint early signs of …


Why 2022 is only the beginning for AI regulation

#artificialintelligence

Earlier this year, the Food and Drug Administration (FDA) released the Artificial Intelligence/Machine Learning-Based Software as a Medical Device ( …


FDA Joins Other Regulators in Focus on AI and Machine Learning – National Law Review

#artificialintelligence

The Food and Drug Administration recently sought comments on the role of transparency for artificial intelligence AI and machine learning-enabled …


FDA Convenes Medical Device Workshop Focused on Artificial Intelligence and Machine …

#artificialintelligence

… Drug Administration (“FDA” or the “Agency”) held a virtual workshop entitled, Transparency of Artificial Intelligence (“AI”)/Machine Learning.


On the Opportunities and Risks of Foundation Models

arXiv.org Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.


FDA leader talks evolving strategy for AI and machine learning validation

#artificialintelligence

And AI is helping healthcare professionals and patients get more insights into how they can translate what we already knew in different silos into …


Patient Safety, Data Privacy Key for Use of AI-Powered Chatbots

#artificialintelligence

… mention of CAs in the US Food and Drug Administration’s (FDA) proposed regulatory framework for AI or machine learning for software as a medical …