Much of the question of FDA regulation of artificial intelligence (AI) and machine learning (ML) is seen as revolving around changes to the statute, but that does not mean the FDA and other agencies are in wait-and-see mode. Representatives of both the FDA and Health Canada said on a March 22 webinar that guidances related to these algorithms will be posted later this year, thus opening the door to a more predictable premarket path for these products.
LONDON – Researchers in the U.K. have applied the heft of national population-level databases to devise a new algorithm that predicts those people who are most at risk of hospitalization and death from COVID-19, despite having received two doses of vaccine.
Radiologists review thousands of images a day. The hope is that artificial intelligence (AI) applications will become useful soon to verify diagnoses, prioritize queued images and even to offer a level of detection and measurement that aren't feasible for humans. One of the latest efforts on this front is by researchers at the University of California at San Francisco (UCSF) and the University of California at Berkeley.
Diagnosis and treatment of infections typically occurs after people exhibit obvious signs of illness, such as fever or a cough. By then, they may already have exposed others and are well on the way to developing more serious symptoms themselves. In the military, such delays can hamper medical countermeasures to contain potential outbreaks and reduce downtime among active duty personnel. Now, Amsterdam-based Royal Philips NV and the U.S. Department of Defense's Defense Threat Reduction Agency and Defense Innovations Unit have built an early warning algorithm – using artificial intelligence – to detect infection before a person shows any signs or symptoms of infection.
Researchers at the Abramson Cancer Center at the University of Pennsylvania have developed an algorithm to better personalize immunotherapy treatment. The algorithm works by examining neoantigen quality, not just their quantity. Neoantigens are proteins that are the result of genetic mutations in a tumor.
Researchers at the Abramson Cancer Center at the University of Pennsylvania have developed an algorithm to better personalize immunotherapy treatment. The algorithm works by examining neoantigen quality, not just their quantity. Neoantigens are proteins that are the result of genetic mutations in a tumor.
Getting on top of the persistent HIV epidemic requires getting ahead of new cases, but only about 7% of at-risk patients have been advised of a prophylactic drug regime approved by the FDA seven years ago. Two new studies appearing in The Lancet HIV suggest that an algorithm that uses electronic health record (EHR) data can help physicians identify their at-risk patients who are good candidates for pre-exposure prophylaxis (PrEP), thus improving the odds that modern medicine might finally put an end to the scourge of acquired immunodeficiency syndrome.
