While regulatory science can lag behind technology advances, the FDA has for the past few years been exploring ways to harness the potential of artificial intelligence (AI) to streamline drug development and the approval process. A nexus for its efforts is the Information Exchange and Data Transformation (INFORMED) initiative anchored in the agency's Oncology Center of Excellence (OCE). At its inception in 2016, INFORMED was designed to tap into the power of big data and advanced analytics to improve disease outcomes.

BEIJING – With home-grown artificial intelligence (AI) medical devices under priority review, mainland China is quickly putting together a regulatory framework to more rapidly tap into the power of AI to develop devices and drugs.

PERTH, Australia – It's likely that Australia will not draft separate guidance or regulations for software applications that use artificial intelligence or machine learning (AI/ML) for drug development or medical devices. Instead, the Therapeutic Goods Administration (TGA) will classify AI and ML under software as a medical device (SaMD) when it is intended for diagnosis, prevention, monitoring or treatment or alleviation of disease.

NEW DELHI – Artificial intelligence (AI) is increasingly gaining a foothold in India's health care landscape, with investors pouring money into the new technology, companies developing products and regulators looking to come up with much-needed rules. India's Ministry of Health has reached out to the public for consultation on its national digital health blueprint that seeks to propel digital health care, including the use of AI in the biotech and medical technology sectors.

The FDA's regulation of artificial intelligence (AI) is divided by product center for reasons that are obvious, but precisely what that regulation will look like is anything but. As the FDA's Center for Devices and Radiological Health (CDRH) goes through the comment period for its discussion draft for AI, other nations are starting their own efforts in this space. The American agency's efforts may foreshadow the approaches employed in other nations.

Screening for early signs of cognitive impairment and dementia amongst the elderly is a task that's often unevenly attended to by primary care physicians. But the routine personal consumer devices that we use every day might offer a clearer and more consistent window into early declines in cognitive and memory function, according to data from a feasibility study that were reported this week at the Association for Computing Machinery's Knowledge, Discovery and Data Mining conference in Anchorage, Alaska.

Information technology and connectivity have transformed productivity and costs in nearly every industry. Health care, however, has remained persistently immune to this transmogrification. Electronic health records (EHRs) have been particularly disappointing on this front, with time-consuming and inconsistent physician data entry as well as poor integration across complex and emerging data sources from medical devices, imaging, genomics and wearables and, as a consequence, a lack of usefulness in improving population health analytics or personalized care.

Deep learning algorithms developed at the Memorial Sloan Kettering Cancer Center (MSK) were able to distinguish prostate, skin and breast cancer with nearly perfect accuracy in a recent clinical trial. The technology has already been licensed exclusively by New York-based startup Paige.AI, which snapped up a $25 million series A early last year to continue to advance it.

With the ongoing push toward value-based care, providers are looking for ways to improve patient outcomes while also lowering health care costs. Los Angeles-based Dearhealth Inc.'s artificial intelligence-powered software-as-a-service (SaaS) platform aims to do meet that demand by helping physicians better manage patients with chronic conditions. Now Philips Health Technology Ventures and other large investors are putting their money behind the company, seeing an opportunity to generate real movement in advance population health.

A fast response with cardiopulmonary resuscitation (CPR) for cardiac arrest victims can save their lives, but older adults often are alone in their home or a bedroom when symptoms strike. Researchers at the University of Washington (UW) have developed a machine learning-based system that listens to ambient audio via dedicated smart speakers or smartphones for agonal breathing, the distinctive sound that a person in cardiac arrest makes.