Can AI Look at Your Retina and Diagnose Alzheimer's? The author of believes AI could bring big changes to the world of medicine. For decades now, it's been fairly well established that once you turn 40 you should start paying more attention to your body. That's when women are supposed to start getting mammograms and men are supposed to start paying a bit more attention to their prostates. Over the next decade, you'll start getting colonoscopies, and from then on out, it feels like a gradual march of doctor's appointments and tests until your body collapses sometime in your seventies or eighties.

Traditional approaches to semantic polarity in computational linguistics treat sentiment as a unidimensional scale, overlooking the multidimensional structure of language. This work introduces TOPol (Topic-Orientation POLarity), a semi-unsupervised framework for reconstructing and interpreting multidimensional narrative polarity fields under human-on-the-loop (HoTL) defined contextual boundaries (CBs). The framework embeds documents using a transformer-based large language model (tLLM), applies neighbor-tuned UMAP projection, and segments topics via Leiden partitioning. Given a CB between discourse regimes A and B, TOPol computes directional vectors between corresponding topic-boundary centroids, yielding a polarity field that quantifies fine-grained semantic displacement during regime shifts. This vectorial representation enables assessing CB quality and detecting polarity changes, guiding HoTL CB refinement. To interpret identified polarity vectors, the tLLM compares their extreme points and produces contrastive labels with estimated coverage. Robustness analyses show that only CB definitions (the main HoTL-tunable parameter) significantly affect results, confirming methodological stability. We evaluate TOPol on two corpora: (i) U.S. Central Bank speeches around a macroeconomic breakpoint, capturing non-affective semantic shifts, and (ii) Amazon product reviews across rating strata, where affective polarity aligns with NRC valence. Results demonstrate that TOPol consistently captures both affective and non-affective polarity transitions, providing a scalable, generalizable, and interpretable framework for context-sensitive multidimensional discourse analysis.

In the much-celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practice of medicine. In the next several decades, he suggests, AI will become sophisticated enough that many of the everyday tasks of physicians could be delegated to it. Topol is perhaps the most articulate advocate of the benefits of AI in medicine, but he is hardly alone in spruiking its potential to allow physicians to dedicate more of their time and attention to providing empathetic care for their patients in the future. Unfortunately, several factors suggest a radically different picture for the future of health care. Far from facilitating a return to a time of closer doctor-patient relationships, the use of medical AI seems likely to further erode therapeutic relationships and threaten professional and patient satisfaction.

An artificial intelligence system has proven it can save lives by warning physicians to check on patients whose heart test results indicate a high risk of dying. In a randomised clinical trial with almost 16,000 patients at two hospitals, the AI reduced overall deaths among high-risk patients by 31 per cent. "This is actually quite extraordinary," says Eric Topol at the Scripps Research Translational Institute in California, who was not involved in the research. "It's very rare for any medication to [produce] a 31 per cent reduction in mortality, and then even more rare for a non-drug – this is just monitoring people with AI." Chin Lin at the National Defense Medical Center in Taiwan and his colleagues first trained their AI on more than 450,000 electrocardiogram (ECG) tests, which measure the heart's electrical activity, along with the survival data of the ECG subjects. The AI learned to produce a percentile score representing each patient's risk of death, with those in at least the 95th percentile considered high risk.

What use could healthcare have for someone who makes things up, can't keep a secret, doesn't really know anything, and, when speaking, simply fills in the next word based on what's come before? Lots, if that individual is the newest form of artificial intelligence, according to some of the biggest companies out there. Companies pushing the latest AI technology -- known as "generative AI" -- are piling on: Google and Microsoft want to bring types of so-called large language models to healthcare. Big firms that are familiar to folks in white coats -- but maybe less so to your average Joe and Jane -- are equally enthusiastic: Electronic medical records giants Epic and Oracle Cerner aren't far behind. The space is crowded with startups, too.

Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. There are few more compelling story lines at the intersection of Wall Street and Fear Street than China's rise to global prominence in the field of artificial intelligence. You don't have to look very far to find a military or financial expert who believes China's AI program will some day surpass the capabilities of its democratic counterparts in Silicon Valley. But, as we've written before, the idea that China is in second place behind the US is a bit misleading. Currently, it would be a huge stretch to call it a race.

A live-stream video of a 76-year-old woman pottering about her kitchen plays on Li Hong's phone. Li is in London, 8,700km from her mother in the Chinese city of Kunming. Li has narrowed the distance between them by installing cameras in her mother's apartment, where she lives alone. The system has built-in microphones and speakers, enabling the pair to discuss the latest readings from the blood pressure monitor of Li's mother, who has a heart condition. "It's like I am back in China with her. The technology is so convenient," says Li.

READY FOR ITS CLOSE-UP: Artificial intelligence has long been hyped as a game changer in health care: Remember this 2012 prediction that computers will replace 80 percent of doctors? But it's been much harder to get a sense of the real-world scale of the phenomenon. Is AI a perpetual technology of the future? Or is it starting to get a toehold? A recently released Food and Drug Administration database starts to get at that question.

This transcript has been edited for clarity. This is Eric Topol with the Medscape Medicine and the Machine podcast. I'm thrilled today to welcome Kai-Fu Lee, who is one of the leading artificial intelligence (AI) experts in the world. Before we get to that, let me give our Medscape audience a little background. You were born in Taiwan. You came to the United States in 1973, went to Columbia University and then Carnegie Mellon University, one of the leading AI centers in the country. You had an amazing career at Apple, Microsoft, and Google, when you led Google in China. In many ways you have been a major force for AI around the world, so we're really interested in your perspective. You and I first converged after I read your book AI Superpowers: China, Silicon Valley, and the New World Order. I was blown away because you had a unique perspective.

If you want to imagine the future of healthcare, you can do no better than to read cardiologist and bestselling author Eric Topol's trilogy on the subject: "The Creative Destruction of Medicine," "The Patient Will See You Now," and "Deep Medicine." "Deep Medicine" bears a paradoxical subtitle: "How Artificial Intelligence Can Make Healthcare Human Again." The book describes the growing interaction of human and machine brains. Topol envisions a symbiosis, with people and machines working together to assist patients in ways that neither can do alone. In the process, healthcare providers will shed some of the mind-numbing rote tasks they endure today, giving them more time to focus on patients.