Former FDA Commissioner Dr. Scott Gottlieb stressed the need for modernizing the clinical trials process in a speech to the Bipartisan Policy Center in January of this year.1 He is quoted as saying, "digital technologies are one of our most promising tools for making healthcare more efficient." Improving efficiency in clinical trial development is only one potential enhancement that can result from the use of machine learning. Machine learning and artificial intelligence (AI) are often used interchangeably, but that assumption is incorrect. Machine learning is the subset of AI that is related to the development of algorithms that can make accurate predictions of future outcomes via pattern recognition and rules-based logic. Such use of logic and algorithms can improve patient selection, provide predictive long-term outcomes, and reduce the time and cost in the execution of clinical trials.

In April 2019, the US Food and Drug Administration (FDA) issued a white paper, "Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device," announcing steps to consider a new regulatory framework to promote the development of safe and effective medical devices that use advanced AI algorithms. AI, and specifically ML, are "techniques used to design and train software algorithms to learn from and act on data." FDA's proposed approach would allow modifications to algorithms to be made from real-world learning and adaptation that accommodates the iterative nature of AI products while ensuring FDA's standards for safety and effectiveness are maintained. Under the existing framework, a premarket submission (i.e., a 510(k)) would be required if the AI/ML software modification significantly affects device performance or the device's safety and effectiveness; the modification is to the device's intended use; or the modification introduces a major change to the software as a medical device (SaMD) algorithm. In the case of a PMA-approved SaMD, a PMA supplement would be required for changes that affect safety or effectiveness.

Tel Aviv is the city with the highest number of startups per capita in the world, according to the 2018 Global Startup Ecosystem report -- more than 6,000, of which 18 are unicorns. The city's tech cluster, dubbed Silicon Wadi, is home to more than 100 venture capital funds, plus hundreds of accelerators and co-working places. "Tel Aviv is transitioning from startup nation to scale-up nation," says Eyal Gura, co-founder of Zebra Medical Vision. Amit Gilon, an investor at Kaedan Capital VC fund, agrees – adding that Israel is not just about successful B2B companies anymore, such as Checkpoint, Nice and Amdocs, but also about "big B2C success stories like Playtika, Wix, Fiverr and others". Founded in 2015, Arbe has built a 4D ultra-high-resolution imaging radar for cars.

If you want to get into your doctor's bad books, turn up to your next appointment having preemptively diagnosed yourself via Google. If there's one thing that annoys healthcare professionals, it's patients thinking that computers can do their job as well as them. Some physicians even have signages in their waiting rooms stating that. Algorithms aren't going to replace our doctor any time soon. However, there's a lot of machine learning in healthcare that can help him diagnose you faster and more efficiently.

Imagine a couple of caffeine-addled biochemistry majors late at night in their dorm kitchen cooking up a new medicine that proves remarkably effective at soothing colds but inadvertently causes permanent behavioral changes. Those who ingest it become radically politicized and shout uncontrollably in casual conversation. Still, the concoction sells to billions of people. This sounds preposterous, because the FDA would never let such a drug reach the market. Olaf J. Groth is founding CEO of Cambrian Labs and a professor at Hult Business School.

RADSpa is Telerad Tech's Next Generation AI Integrated Radiology Workflow Platform with an Integrated RIS PACS, designed to scale from a standalone diagnostics center to large-scale Multi-Site, Multi-Geography radiology centers & hospitals. RADSpa is available in Cloud, Enterprise, and OEM Licensing models. It is currently deployed in more than 24 countries with highly advanced Analytics and Workflow Orchestration capabilities. It supports flexible radiology needs with customizable and dynamic workflows enabling seamless delivery across borders. It's enhanced Patient Security Framework enables secured and anonymized cross-border study transmission and reporting.

You could be forgiven for thinking that AI will soon replace human physicians based on headlines such as "The AI Doctor Will See You Now," "Your Future Doctor May Not Be Human," and "This AI Just Beat Human Doctors on a Clinical Exam." But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of AI services working alongside human clinicians. There is no shortage of optimism about AI in the medical community. But many also caution the hype surrounding AI has yet to be realized in real clinical settings. There are also different visions for how AI services could make the biggest impact.

Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost could lead to reduced healthcare costs for a patient. In addition, a precisely learned molecule representation in a DTI model could contribute to developing personalized medicine, which will help many patient cohorts. In this paper, we propose a new molecule representation based on the self-attention mechanism, and a new DTI model using our molecule representation. The experiments show that our DTI model outperforms the state of the art by up to 4.9% points in terms of area under the precision-recall curve. Moreover, a study using the DrugBank database proves that our model effectively lists all known drugs targeting a specific cancer biomarker in the top-30 candidate list.

The Food and Drug Administration has to recall hundreds of foods every year. Like cookie snack packs with chunks of blue plastic hiding inside, Salmonella-tainted taco seasoning or curry powder laced with lead. It can take months before a recall is issued. But now researchers have come up with a method that might fast-track that process, leading to early detection and, ultimately, faster recalls. The system relies on the fact that people increasingly buy foods and spices online.

The Food and Drug Administration has allowed medical devices that rely on artificial intelligence algorithms onto the market, but so far, the agency has given the green light only to devices with "locked algorithms" -- those that remain the same as the product is used until they're updated by the manufacturer. Systems with algorithms that evolve and sharpen on their own, however, are already in development. 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.