Biofourmis, a fast-growing global leader in digital therapeutics, has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its machine-learning and artificial intelligence (AI)-powered Biovitals Analytics Engine as a medical device for ambulatory physiological monitoring. This regulatory approval of the Biovitals Analytics Engine is part of FDA's growing recognition of machine-learning and AI in the Software as a Medical Device category. "This milestone approval is foundational to the Biovitals ecosystem, which includes not only our most advanced solution, BiovitalsHF for heart failure--but also our range of solutions across therapeutic areas, such as pain, oncology, sleep disorders and others in development," said Kuldeep Singh Rajput, CEO and founder of Biofourmis. "Receiving this important regulatory approval will only accelerate the development and commercialization of these innovative digital therapeutic solutions." This FDA approval is the second market authorization for Biofourmis, having earned the agency's approval in May 2019 for its Biovitals RhythmAnalytics platform, which is cloud-based software for automated interpretation of more than 15 types of cardiac arrhythmias.

Matt DeCamp, associate professor with the Center for Bioethics and Humanities from CU Anschutz, framed the AI landscape: Up to $6 billion anticipated for AI investment into biomedical research by 2021 At least 14 recent AI-related FDA approvals in past two years, mostly in imaging, ophthalmology and pathology 55 active or pending clinical trials using the term "deep learning" 141 startup biotech companies using AI Insurance companies actively using AI to review records and optimize care for chronic conditions

On September 26, 2019, the US Food and Drug Administration (FDA) published six guidance documents clarifying its scope of authority and enforcement discretion policies in light of the 21st Century Cures Act (Cures Act). The long-awaited draft guidance on Clinical Decision Support (CDS) software sets forth FDA's proposed approach to regulating CDS, including software that incorporates machine learning (ML) technology. Companies developing ML software for life science applications should consider reviewing FDA's planned approach to inform their regulatory strategies. In a long-awaited move, FDA published a draft guidance on CDS software. With the rise of artificial intelligence (AI) and machine learning (ML), CDS presents a novel opportunity to analyze immensely large amounts of data for patterns or other information that may be relevant to a particular patient's diagnosis.

Medical devices used outside of acute care settings, such as point-of-care ultrasound, and those whose rapid development has outpaced safety assessments, namely robots, are among the top health technology hazards that nonprofit ECRI Institute has identified in a new report. Leading the organization's list, however, are accidents associated with a decades-old technology, surgical staplers. Problems associated with staplers have led FDA to propose reclassifying the devices from Class I to Class II, a higher-risk category that would allow the agency to establish special controls and labeling requirements for the devices. An advisory panel for the agency endorsed the proposal in May, and Medtronic and the Society of American Gastrointestinal and Endoscopic Surgeons both have voiced support for formal reclassification. ECRI annually compiles a list of its 10 biggest safety concerns from incident investigations and device testing as well as public and private event reporting databases.

A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks. In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium. Deng, an assistant professor of food microbiology at the center, and Shaokang Zhang, a postdoctoral associate with the center, led the project, which also included experts from the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute. According to the Foodborne Disease Outbreak Surveillance System, close to 3,000 outbreaks of foodborne illness were reported in the U.S. from 2009 to 2015. Of those, 900 -- or 30 percent -- were caused by different serotypes of Salmonella, including Typhimurium, Deng said.

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3 Medical AI may be trained in inappropriate environments, using imperfect techniques, or on incomplete data. Even when algorithms are trained as well as possible, they may, for example, miss a tumor in a radiological image or suggest the incorrect dose for a drug or an inappropriate drug.

Artificial intelligence is all the rage in healthcare as companies look for tech-driven ways to cut costs and promote patient health. Tech giants like Intel, Google, Amazon, Microsoft and Apple have swooped in to assist payers and providers with their efforts to join the fast-paced environment. Santa Clara, California-based Intel boasts partnerships across myriad sectors in healthcare. For example, earlier this year, not-for-profit integrated health system Sharp HealthCare, which is based in San Diego, used Intel's predictive analytics capabilities to alert its rapid-response team to identify high-risk patients before a health crisis occurred. And currently, Intel is working with pharmaceutical company Novartis on deep neural networks to accelerate content screening in drug discovery.

A new automated data analytics program is crucial for the early detection of signals and predications for regulated chemicals that may pose highly hazardous health risks at the Food and Drug Administration. The agency's Center for Food Safety and Applied Nutrition first initiated the project, called the Emerging Chemical Hazard Intelligence Platform. It allows the center to anticipate potential chemicals associated with adverse health events before they get out of control, explained its Office of Food Additive Safety's Informatics and Information Systems Lead and Senior Policy Advisor Ernest Kwegyir-Afful. "Every time we have one these big [food safety] incidents, we have to drop everything so we can actually deal with it," said Kwegyir-Afful at SAS' Unleash Analytics: Making AI & Analytics Real event Aug. 20. This includes U.S. food supply chemical incidents, such as detecting products that increase the production of melanin in babies to measuring arsenic toxicity, he said.

The FDA has approved a mobile chest x-ray system that helps detect pneumothorax – a type of collapsed lung – by flagging critical cases for triage. According to the manufacturer, GE Healthcare, Critical Care Suite is the first system of its kind to get approved by the FDA, offering a series of artificial intelligence algorithms embedded on a mobile X-ray device. Pneumothorax, the presence of gas within the pleural space between the lung and chest wall, is a problem in hospitals across the world. But the challenge is to quickly identify real cases from the suspected ones, which can be difficult in busy hospitals. But missing cases of pneumothorax can lead to total lung collapse and other potentially fatal complications, with misdiagnosis or late diagnosis affecting around 74,000 people in the US annually.

The Food and Drug Administration has issued new guidelines on how it will regulate mobile health software and products that use artificial intelligence to help doctors decide how to treat patients. The guidelines, contained in a pair of documents released Thursday morning, clarify the agency's intent to focus its oversight powers on AI decision-support products that are meant to guide treatment of serious or critical conditions, but whose rationale cannot be independently evaluated by doctors. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis. Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.