Machine learning could improve medicine by analyzing data to improve diagnoses and target cures, but technological, bureaucratic, and regulatory …

Artificial intelligence is spreading into health care, often as software or a computer program capable of learning from large amounts of data and making predictions to guide care or help patients. Investors see health care's future as inextricably linked with artificial intelligence. That's obvious from the cash pouring into AI-enabled digital health startups, including more than $3 billion in the first half of 2022 alone and nearly $10 billion in 2021, according to a Rock Health investment analysis commissioned by POLITICO. And no wonder, considering the bold predictions technologists have made. At a conference in 2016, Geoffrey Hinton, British cognitive psychologist and "godfather" of AI, said radiologists would soon go the way of typesetters and bank tellers: "People should stop training radiologists now. It's just completely obvious that, within five years, deep learning is going to do better."

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. The health care industry is starting to adopt digital twins to improve personalized medicine, health care organization performance, and new medicines and devices. Although simulations have been around for some time, today's medical digital twins represent an important new take. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and health care organizations, as well as drug and device manufacturers.

Our daily interactions with technology create vast amounts of data and analytics giving rise to what has been dubbed the "artificial intelligence revolution." Now, a new research centre at the University of Toronto's Temerty Faculty of Medicine aims to harness the incredible promise of AI in the realms of medicine and health care. The Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM) launched this week at U of T, solidifying Toronto's place at the nexus of AI, data science and the health sciences. "Toronto is uniquely positioned to lead globally in artificial intelligence in healthcare," says Professor Muhammad Mamdani, who was recently appointed the inaugural director of T-CAIREM for a five-year term. "Our expertise in medicine and allied health sciences, computer science, statistics, mathematics and engineering is among the best in the world."

Artificial intelligence has the potential to transform health care. It can enable health care professionals to analyze health data quickly and precisely, and lead to better detection, treatment, and prevention of a multitude of physical and mental health issues. Artificial intelligence integrated with virtual care -- telemedicine and digital health -- interventions are playing a vital role in responding to Covid-19. Penn Medicine, for example, has designed a Covid-19 chatbot to stratify patients and facilitate triage. Penn is also using machine learning to identify patients at risk for sepsis.

As an industry reliant on patient records and beset by outdated technology, health care is widely thought to be a prime target for an artificial intelligence revolution. Many believe the technology will provide a host of benefits to clinical practitioners, speeding up the overall experience and diagnosing illnesses early on to identify potential treatment. Just two days ago, DeepMind, an AI (artificial intelligence) firm owned by Google, said it had lent its technology to London's Moorfields Eye Hospital for groundbreaking research into detecting eye diseases. It was used to scan and identify more than 50 ophthalmological conditions. DeepMind's machine-learning technology made correct diagnoses 94 percent of the time, Moorfields said. The development indicated that AI can analyze health problems with as much accuracy as a doctor.

Health care applications of machine learning and AI have been in the news a bit more than usual recently, concurrent with the recent Healthcare Information and Management Systems Society (HIMSS) conference in Las Vegas. HIMSS is a 45,000 attendee conference dedicated to health care IT. Surprising no one, AI was a major theme at this year's event. There was a whole sub-conference focused on ML and AI, plus a ton of AI-focused sessions in the regular conference and a good number of announcements by industry leaders and startups alike. I've only done a couple of health care-focused shows on my podcast so far, but I'm planning to dive into this area more deeply this year.

Providers now need to start preparing for "machine learning" medicine, two professors write in a New England Journal of Medicine perspective. Ziad Obermeyer, an assistant professor at Harvard Medical School, and Ezekiel Emanuel, chair of the Department of Medical Ethics and Health Policy at University of Pennsylvania, write that computers eventually will be more effective than human providers at making a range of health care decisions--from determining the most cost-effective surgical supplies to deciding which tests a patient can skip. For example, they predict computer programs will be able to analyze radiographs for anomalies at a pixel level, a much more precise level of detail than the human eye is capable of detecting. "We can point this very powerful tool at a medical problem and say, 'I'm going to show you a bunch of people who had heart attacks, and a bunch who didn't. Go learn how to tell them apart,'" Obermeyer says in an interview with STAT News.

You may be familiar with the name Robert Wachter, M.D. He's written six books and hundreds of journal articles; he chairs the department of medicine at the University of California, San Francisco; and he's a leading advocate for patient safety. He's perhaps best known for having coined the term "hospitalist," and for having defined and promoted hospital medicine as a recognized primary care subspecialty. In a new book, The Digital Doctor: Hope, Hype and Harm at the Dawn of Medicine's Computer Age (McGraw-Hill Education, 2015), Wachter takes a deep dive into the turbulent waters of medical technology as informed by artificial intelligence, or AI. "While computers are preventing many medical errors," he observes, "they are also causing new kinds of mistakes, some of them whoppers." It was one of those -- at his own institution, UCSF -- that inspired Wachter to examine where AI has got us and where it's taking us.