When Regina Barzilay had a routine mammogram in her early 40s, the image showed a complex array of white splotches in her breast tissue. The marks could be normal, or they could be cancerous--even the best radiologists often struggle to tell the difference. Her doctors decided the spots were not immediately worrisome. In hindsight, she says, "I already had cancer, and they didn't see it." Over the next two years Barzilay underwent a second mammogram, a breast MRI and a biopsy, all of which continued to yield ambiguous or conflicting findings. Ultimately she was diagnosed with breast cancer in 2014, but the path to that diagnosis had been unbelievably frustrating. "How do you do three tests and get three different results?" she wondered.

After years of development, machine learning methods have matured enough to be used in clinical medicine. In 2018 the FDA approved software to screen patients for diabetic retinopathy, and the methods are rapidly making their way into other applications for image analysis, natural language processing, EHR data mining, drug discovery, and more. JAMA is proud to be a primary forum for the work of interdisciplinary groups demonstrating the use of machine learning methods for clinical medicine and health care. To understand the work read JAMA's Users' Guide to the Medical Literature How to Read Articles That Use Machine Learning, authored by Google Health scientists, and an accompanying commentary. See also JAMA Network's Health Informatics collection.

Federal guidance on artificial intelligence needs additions to ensure the U.S. has a seat at the international table. The rapid proliferation of applications of artificial intelligence and machine learning--or AI, for short--coupled with the potential for significant societal impact has spurred calls around the world for new regulation. The European Union and China are developing their own rules, and the Organization for Economic Cooperation and Development has developed principles that enjoy the support of its members plus a handful of other countries. In January, the U.S. Office of Management and Budget (OMB) also issued its own draft guidance, ensuring the United States a seat at the table during this ongoing, multi-year, international conversation. The U.S. guidance--covering "weak" or narrow AI applications of the kind we experience today--reflects a light-touch approach to regulation, consistent with a desire to reward U.S. ingenuity.

This model was designed to look for chemical features that make molecules effective at killing E.coli in a process that involved training on 2,500 molecules including 1,700 FDA approved drugs and a set of 800 natural products with diverse structures and a range of bioactivities. Once trained it was tested on a library of 6,000 compounds, and the model picked out one molecule predicted to have strong antibacterial activity and chemical structure different from any existing antibiotics. Then using a different machine deep learning algorithm model the newly identified Halicin molecule was shown to likely have low toxicity to human cells.

The medical and healthcare categories have been leading robotic innovation for decades. Look no further than Intuitive Surgical, whose da Vinci robot has been performing surgery since it received FDA clearance in the early 2000s. These days, the SRI spinoff is currently valued at more than $60 billion. There's a lot of money to be made for established companies and still areas to be explored for young startups, both on and off the operating table. The venture community has been betting big on companies developing everything from new surgical robots, assistive robots for medical facilities, robotic medical aid devices or otherwise.

The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered." In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.

Bacteria are evolving resistance to antibiotics much faster than new drugs can be developed, potentially leading us to a dangerous future where infections are more likely to be deadly. Now, an artificial intelligence model has identified a powerful new antibiotic called halicin, which cleared infections of most superbugs in mouse tests. Ever since antibiotics were invented in the early 20th century, we've been locked in an arms race with bacteria. Antibiotics work for a while, but eventually the bugs evolve resistance to those in wide use. Scientists develop new ones, so bacteria continue to evolve, and so on.

Researchers at Massachusetts Institute of Technology (MIT) have harnessed a machine-learning algorithm to identify a new antibiotic compound that, in laboratory tests, killed many of the world's most challenging disease-causing bacteria, including some strains that are resistant to all known antibiotics. The new antibiotic candidate, which has been given the name halicin--after the fictional artificial intelligence system from "2001: A Space Odyssey,"--was discovered in the Drug Repurposing Hub, and is structurally different to conventional antibiotics. Initial in vivo experiments showed that halicin was effective against Clostridium difficile and pan-resistant Acinetobacter baumannii infections in two mouse models. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," said James Collins, PhD, the Termeer professor of medical engineering and science in MIT's Institute for Medical Engineering and Science (IMES) and department of biological engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered."

"Between 12 to 18 million Americans every year will experience some sort of diagnostic error," said Paul Cerrato, a journalist and researcher. "So the question is: Why such a huge number? And what can we do better in terms of reinventing the tools so they catch these conditions more effectively?" Cerrato is co-author, alongside Dr. John Halamka, newly minted president of Mayo Clinic Platform, of the new HIMSS Book Series edition, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. At HIMSS20, the two of them will discuss the book, and the bigger picture around CDS tools that are fast being transformed by the advent of artificial intelligence, machine learning and big data analytics.

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.