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.

Scientists are trying to understand why. New book explores the philosophy of addiction. Our understanding of addiction is changing. Breakthroughs, discoveries, and DIY tips sent every weekday. Reprinted by permission of Princeton University Press.

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency Department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data.

Patients with co-morbidities often require multiple medications to manage their conditions. However, existing medication recommendation systems only offer class-level medications and regard all interactions among drugs to have the same level of severity. This limits their ability to provide personalized and safe recommendations tailored to individual needs. In this work, we introduce a deep learning-based fine-grained medication recommendation system called REFINE, which is designed to improve treatment outcomes and minimize adverse drug interactions. In order to better characterize patients' health conditions, we model the trend in medication dosage titrations and lab test responses, and adapt the vision transformer to obtain effective patient representations. We also model drug interaction severity levels as weighted graphs to learn safe drug combinations and design a balanced loss function to avoid overly conservative recommendations and miss medications that might be needed for certain conditions. Extensive experiments on two real-world datasets show that REFINE outperforms state-of-the-art techniques.

Grindr Goes'AI-First' as It Strives to Be an'Everything App for the Gay Guy' After controlling shareholders failed to take Grindr private and controversies over data and the banning of the phrase "No Zionists," Grindr's CEO opens up about AI, privacy, and big expansion plans. Every Grindr user is unique. South Koreans prefer open relationships. The highest percentage of self-proclaimed "daddies" call the US home, and Switzerland is overrun with twinks. Delivered by annual trend report Grindr Unwrapped, those critical insights offer the type of information that will help usher the company into its "AI-first" era where it's "the everything app for the gay guy," CEO George Arison tells WIRED. Grindr was the first to leverage geo-location tech when it burst onto the scene in 2009. Arison arrived at the company in 2022 from the world of automotive ecommerce.

The rapid advancement of large language models (LLMs) has accelerated their integration into clinical decision support, particularly in prescription review. To enable systematic and fine-grained evaluation, we developed RxBench, a comprehensive benchmark that covers common prescription review categories and consolidates 14 frequent types of prescription errors drawn from authoritative pharmacy references. RxBench consists of 1,150 single-choice, 230 multiple-choice, and 879 short-answer items, all reviewed by experienced clinical pharmacists. We benchmarked 18 state-of-the-art LLMs and identified clear stratification of performance across tasks. Notably, Gemini-2.5-pro-preview-05-06, Grok-4-0709, and DeepSeek-R1-0528 consistently formed the first tier, outperforming other models in both accuracy and robustness. Comparisons with licensed pharmacists indicated that leading LLMs can match or exceed human performance in certain tasks. Furthermore, building on insights from our benchmark evaluation, we performed targeted fine-tuning on a mid-tier model, resulting in a specialized model that rivals leading general-purpose LLMs in performance on short-answer question tasks. The main contribution of RxBench lies in establishing a standardized, error-type-oriented framework that not only reveals the capabilities and limitations of frontier LLMs in prescription review but also provides a foundational resource for building more reliable and specialized clinical tools.

In this paper, we address this gap by dividing a typical patient's clinical journey

However, existing medication recommendation systems only offer class-level medications and regard all interactions among drugs to have the same level of severity.