As patients and employers look for alternatives to pricey GLP-1 drugs, Silicon Valley startup Twin Health is using AI and wearable sensors to help people make healthier choices. Rodney Buckley has lost 100 pounds in less than a year, not by using a GLP-1 drug but with the help of a digital twin. Last March, the 55-year-old retired firefighter turned village mayor of Third Lake, Illinois, was 376 pounds. He had tried different diets over the years and would typically lose some weight but eventually gain it back. When his wife's employer started offering a program from startup Twin Health, he thought he would give it a try.

Signos is the first FDA-cleared, AI-enabled system that uses CGMs to nudge you towards healthier behaviors. According to the American Diabetes Association, around 7 million people in the United States are undiagnosed, with 1 in 3 Americans at risk for developing type 2 diabetes. If you do not go on medication, you can manage the condition--a chronic metabolic disease that's characterized by elevated blood sugar levels--by exercising and watching what you eat (very, very closely). In the past few years, the tools that diabetics use to help manage their condition have become more widely available. Continuous glucose monitors (CGMs) like the Abbott Lingo and the Dexcom Stelo used to be available only by prescription.

Gene-edited pancreatic cells have been transplanted into a patient with type 1 diabetes for the first time. They produced insulin for months without the patient needing to take immunosuppressants. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Crispr gene-editing technology has demonstrated its revolutionary potential in recent years: It has been used to treat rare diseases, to adapt crops to withstand the extremes of climate change, or even to change the color of a spider's web.

Given their ability for advanced reasoning, extensive contextual understanding, and robust question-answering abilities, large language models have become prominent in healthcare management research. Despite adeptly handling a broad spectrum of healthcare inquiries, these models face significant challenges in delivering accurate and practical advice for chronic conditions such as diabetes. We evaluate the responses of ChatGPT versions 3.5 and 4 to diabetes patient queries, assessing their depth of medical knowledge and their capacity to deliver personalized, context-specific advice for diabetes self-management. Our findings reveal discrepancies in accuracy and embedded biases, emphasizing the models' limitations in providing tailored advice unless activated by sophisticated prompting techniques. Additionally, we observe that both models often provide advice without seeking necessary clarification, a practice that can result in potentially dangerous advice. This underscores the limited practical effectiveness of these models without human oversight in clinical settings. To address these issues, we propose a commonsense evaluation layer for prompt evaluation and incorporating disease-specific external memory using an advanced Retrieval Augmented Generation technique. This approach aims to improve information quality and reduce misinformation risks, contributing to more reliable AI applications in healthcare settings. Our findings seek to influence the future direction of AI in healthcare, enhancing both the scope and quality of its integration.

Effective diabetes management relies heavily on the continuous monitoring of blood glucose levels, traditionally achieved through invasive and uncomfortable methods. While various non-invasive techniques have been explored, such as optical, microwave, and electrochemical approaches, none have effectively supplanted these invasive technologies due to issues related to complexity, accuracy, and cost. In this study, we present a transformative and straightforward method that utilizes voice analysis to predict blood glucose levels. Our research investigates the relationship between fluctuations in blood glucose and vocal characteristics, highlighting the influence of blood vessel dynamics during voice production. By applying advanced machine learning algorithms, we analyzed vocal signal variations and established a significant correlation with blood glucose levels. We developed a predictive model using artificial intelligence, based on voice recordings and corresponding glucose measurements from participants, utilizing logistic regression and Ridge regularization. Our findings indicate that voice analysis may serve as a viable non-invasive alternative for glucose monitoring. This innovative approach not only has the potential to streamline and reduce the costs associated with diabetes management but also aims to enhance the quality of life for individuals living with diabetes by providing a painless and user-friendly method for monitoring blood sugar levels.

Facebook founder Mark Zuckerberg reportedly loves conducting meetings while walking, and so did Apple founder Steve Jobs - and scientists have shown that they were right on target. Just 20 minutes of walking can prepare the brain to take in and retain new information, neuroscience research has shown. These positive effects can be seen in areas of the brain involved in making decisions, managing stress, and planning our behavior. Other forms of exercise have their own benefits on brain health, too, but this research determined that it doesn't take much to boost your brain power - and a little bit of walking is much better than no exercise at all. Just 20 minutes of walking can prepare the brain to take in and retain new information, neuroscience research has shown.

Chrissy Kinsella was looking for a more personalized approach to her health. "You know, what is good for you as an individual may not necessarily be good for the next person," she says. So she reached for a subscription to Zoe--a personalized nutrition service cofounded by Tim Spector, a celebrity scientist and a genetic epidemiologist at King's College London. Kinsella paid the £299 ($365) for a testing kit and later received a bright yellow package in the mail: a bundle of vials, patches, and muffins. By testing, scoring, and monitoring how you respond to different foods, Zoe says, it can help with a whole host of problems.

The week before heading off to college, Harry Emerson was diagnosed with type 1 diabetes. Without the ability to produce insulin, the hormone that transports blood sugar to fuel other cells, he'd need help from medical devices to survive, his doctors told him. Eager to get on with school, Emerson rushed through the process of familiarizing himself with the technology, then went off to university. Because people with type 1 diabetes make very little or no insulin on their own, they need to keep careful track of their blood sugar as it changes throughout the day. They inject insulin when their blood sugar is too high or when it's about to spike after a meal and keep fast-acting carbs ready to eat when it dips too low.

The management of people with long-term or chronic illness is one of the biggest challenges for national health systems. In fact, these diseases are among the leading causes of hospitalization, especially for the elderly, and huge amount of resources required to monitor them leads to problems with sustainability of the healthcare systems. The increasing diffusion of portable devices and new connectivity technologies allows the implementation of telemonitoring system capable of providing support to health care providers and lighten the burden on hospitals and clinics. In this paper, we present the implementation of a telemonitoring platform for healthcare, designed to capture several types of physiological health parameters from different consumer mobile and custom devices. Consumer medical devices can be integrated into the platform via the Google Fit ecosystem that supports hundreds of devices, while custom devices can directly interact with the platform with standard communication protocols. The platform is designed to process the acquired data using machine learning algorithms, and to provide patients and physicians the physiological health parameters with a user-friendly, comprehensive, and easy to understand dashboard which monitors the parameters through time. Preliminary usability tests show a good user satisfaction in terms of functionality and usefulness.

DayTwo is just one of a host of apps claiming to offer A.I. eating solutions. Instead of a traditional diet, which often has a set list of "good" and "bad" foods, these programs are more like personal assistants that help someone quickly make healthy food choices. They are based on research showing that bodies each react differently to the same foods, and the healthiest choices are likely to be unique to each individual. Whether these A.I. nutritionists are ready for widespread use is still unclear, and there is very little research available from sources outside the companies selling apps. Users should be wary of overly broad claims that go beyond predicting how foods affect blood sugar.