All articles tagged with #clinical decision support
The FDA announced plans to ease regulation of digital health products, including AI-enabled clinical decision support software, to promote innovation and faster market entry, signaling a shift towards more Silicon Valley-style regulation in healthcare technology.
The article emphasizes the urgent need for medical schools and health systems to integrate AI literacy and protocols into clinical training, advocating for AI as a vital tool for doctors to enhance patient care and decision-making, especially in underserved areas, rather than restricting its use.
OpenEvidence, a rapidly growing medical AI platform used by over 40% of U.S. physicians, announced a $210 million Series B funding round at a $3.5 billion valuation, and launched OpenEvidence DeepConsult™, an AI agent designed to assist physicians with advanced medical research, aiming to improve clinical decision-making and patient outcomes.
A new study warns that physicians are not adequately prepared for the integration of artificial intelligence (AI) tools, such as clinical decision support (CDS) algorithms, into clinical practice. The success of these technologies depends on physicians' understanding of these tools, which is currently lacking. The study recommends targeted training and a hands-on learning approach to enhance physicians' understanding of AI tools. The University of Maryland School of Medicine has proposed incorporating explicit coverage of probabilistic reasoning tailored to CDS algorithms in medical education and clinical training. The launch of the Institute for Health Computing aims to educate and train healthcare providers on the latest technologies, including AI, to enhance disease diagnosis, prevention, and treatment.
Artificial intelligence (AI) devices may soon take on the roles of doctors, with machine learning and natural language processing being the main technologies driving this transformation. AI tools are already being used in healthcare for administrative functions, image analysis, clinical decision support, and even fully automated diagnosis and treatment. While regulatory challenges and concerns about errors in training datasets remain, the potential of AI in improving healthcare outcomes and addressing financial challenges is significant. As the quality and scope of clinical data continue to grow, these tools will increasingly enhance the productivity of providers and may even substitute for them in certain cases.