High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained

Robert F. Kennedy Jr. is an AI guy. Last week, during a stop in Nashville on his Take Back Your Health tour, the Health and Human Services secretary brought up the technology between condemning ultra-processed foods and urging Americans to eat protein. "My agency is now leading the federal government in driving AI into all of our activities," he declared. An army of bots, Kennedy said, will transform medicine, eliminate fraud, and put a virtual doctor in everyone's pocket. RFK Jr. has talked up the promise of infusing his department with AI for months.

Flow Neuroscience receives FDA approval for FL-100 depression device showing 58% symptom improvement. The home-use brain stimulation headset will be available in 2026.

Brewing byproduct may be a key sustainable secret ingredient. Breakthroughs, discoveries, and DIY tips sent every weekday. Brewing beer relies on a very simple living thing-brewer's yeast. The microorganisms thrive on mashed grains, converting sugars into both alcohol and carbon dioxide along the way. But there's not much use for yeast after the pints are poured .

Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the opportunity to study rare exposures and outcomes. However, these studies are complicated by the need to preserve the privacy of each individual's data, heterogeneity in their covariate distributions, and different data structures between sites. We propose a novel federated approach to derive valid causal inferences for a target population using multi-site data. We adjust for covariate shift and accommodate covariate mismatch between sites by developing a multiply-robust and privacy-preserving nuisance function estimation approach. Our methodology incorporates transfer learning to estimate ensemble weights to combine information from source sites. We show that these learned weights are efficient and optimal under different scenarios. We showcase the finite sample advantages of our approach in terms of efficiency and robustness compared to existing state-of-the-art approaches. We apply our approach to study the treatment effect of percutaneous coronary intervention (PCI) on the duration of hospitalization for patients experiencing acute myocardial infarction (AMI) with data from the Centers for Medicare \& Medicaid Services (CMS).

Drug-induced toxicity is one of the leading reasons new drugs fail clinical trials. Machine learning models that predict drug toxicity from molecular structure could help researchers prioritize less toxic drug candidates. However, current toxicity datasets are typically small and limited to a single organ system (e.g., cardio, renal, or liver). Creating these datasets often involved time-intensive expert curation by parsing drug labelling documents that can exceed 100 pages per drug. Here, we introduce UniTox, a unified dataset of 2,418 FDA-approved drugs with drug-induced toxicity summaries and ratings created by using GPT-4o to process FDA drug labels.

Experts urge caution as Trump's big bill incentivizes AI in healthcare For states to receive certain funding stipulated in the Trump administration's "big, beautiful" bill, they must meet three of 10 criteria - including integrating more artificial intelligence ( AI) technology in healthcare settings - which experts say could have major benefits and liabilities for under-resourced hospitals, depending on how it's implemented. The Rural Health Transformation Fund is a carveout that will provide $50bn over a period of five years to states who meet certain application criteria, including "consumer-facing, technology-driven solutions for the prevention and management of chronic diseases," and "providing training and technical assistance for the development and adoption of technology-enabled solutions that improve care delivery in rural hospitals, including remote monitoring, robotics, artificial intelligence, and other advanced technologies". Analysts have noted that this $50bn will not be nearly enough to make up for the Congressional Budget Office's projected $911bn reduction in Medicaid spending over the next decade under the bill (Obba). These cuts will affect both patients who lose free health coverage under Medicaid, and hospitals who benefit from those patients' Medicaid reimbursements. Chenhao Tan, associate professor of data science at the University of Chicago, and Karni Chagal-Feferkorn, an assistant professor at the University of South Florida's college of AI and cybersecurity, said AI technology could provide major benefits to rural hospitals that are frequently under-resourced and under-staffed.

Serving as an emerging and powerful tool, Large Language Model (LLM) - driven Human Digital Twins are showing great potential in healthcare system research. However, its actual simulation ability for complex human psychological traits, such as distrust in the healthcare system, remains unclear. This research gap particularly impacts health professionals' trust and usage of LLM - based Artificial Intelligence (AI) systems in assisting their routine work. In this study, based on the Twin-2K-500 dataset, we systematically evaluated the simulation results of the LLM-driven human digital twin using the Health Care System Distrust Scale (HCSDS) with an established human-subject sample, analyzing item-level distributions, summary statistics, and demographic subgroup patterns. Results show ed that the simulated responses by the digital twin were significantly more centralized with lower variance and had fewer selections of extreme options (all p<0.001) . While the digital twin broa dly reproduces human results in major demographic patterns, such as age and gender, it exhibits relatively low sensitivity in capturing minor differences in education levels. The LLMbased digital twin simulation has the potential to simulate population trends, but it also presents challenges in making detailed, specific distinction s in subgroups of human beings. This study suggests that the current LLM - driven Digital Twins have limitations in modeling complex human attitudes, which require careful calibration and validation before applying them in inferential analyses or policy simulations in health systems engineering. Future studies are necessary to examine the emotional reasonin g mechanism of LLMs before their use, particularly for studies that involve simulations sensitive to social topics, such as human-automation trust.