More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Do not try this at home. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . X-ray imaging is best left to the professionals for a good reason.

The company is rolling out its ChatGPT Health feature to users in the US who are 18 years or older. OpenAI is rolling out its dedicated health experience, ChatGPT Health, to users who are 18 years or older and live in the US. The company began testing ChatGPT Health in January in a separate portal, but this updated version lets ChatGPT reference relevant health data in any conversation. The new feature is designed to go beyond the chatbot's existing ability to answer health-related questions -- something OpenAI claims its new GPT-5.6 Sol model improves -- and grounds responses in personal data from doctors' notes, blood tests and more.

The FDA could decide whether to loosen rules around peptide production this week. Some telehealth players are already seeing dollar signs. Has anyone tried to sell you peptides yet? Once a niche interest among bodybuilders and biohackers, peptides are an increasingly popular class of substance hyped by both fringe influencers and the current US Secretary of Health and Human Services. Earlier this year, Robert F. Kennedy Jr. told the podcaster Joe Rogan that the government intended to loosen restrictions on peptide production.

Report finds secrecy impacted handling of nuclear test veterans' records Image caption, Nuclear test veterans met then Labour opposition leader, Sir Keir Starmer, in 2021, campaigning for full disclosure of the impact of the UK's nuclear bomb testing programme. High levels of Cold War secrecy were a factor in the haphazard approach taken towards the medical records of nuclear test veterans, an official report has found. The lack of a centralised approach means that the system will appear complex and inconsistent to the veterans who have been campaigning for the full release of their medical records. The findings come in a series of official documents released by the Ministry of Defence (MoD) in response to years of campaigning by the veterans. Andy Burnham spoke up in favour of the nuclear test veterans on Tuesday evening in his first speech in parliament since his re-election last month, endorsing their call for a special tribunal. As many as 25,000 members of the UK armed services, scientists and civilians took part in the nuclear testing programme between 1952 and 1967 at sites including Australia and several islands in the Pacific Ocean.

Welcome to the "Temu experience of telehealth," where everyone from Grindr to MAGA influencers can open a virtual clinic selling weight loss drugs and more. This May, the digital search company JustAnswer made an odd pivot: It started selling weight loss drugs. Launching an online pharmacy to peddle GLP-1s wasn't the obvious next step for a business that offers paid guidance from experts, but chief executive Andy Kurtzig says the decision was partly driven by advice from ChatGPT and partly by avid customer interest. The number of queries related to the drugs more than doubled between 2024 and 2025, he says. Plus, it was easy to find help: A company called WhiteLabelMD handles customer service, provides software, and connects patients with clinicians who prescribe drugs like semaglutide and tirzepatide.

A growing number of techniques leverage the spatial structures that underlie many real-world datasets. Despite these advances, the complementary task of estimating spatial structures and understanding their role within these techniques has often been overlooked. In neurophysiological data analysis specifically, numerous methods exist to estimate brain connectivity, but most are not explicitly model-based, dynamic, multivariate, or directed. To address these limitations, we previously introduced noise-driven heat modelling on graphs for neurophysiological connectivity estimation. In this study, we extend this framework by relaxing earlier noise assumptions and adding regularisation to improve robustness. We also develop a simulation procedure to characterise and evaluate our technique in a controlled setting. Finally, we demonstrate that the technique is able to capture meaningful spatial structure across two experiments, each using two real-world datasets. The explicit model formulation of our connectivity estimator has the potential to improve the interpretability of graph-based techniques across a wide range of applications. The code implementing our method is available at https://github.com/sgoerttler/Heat_Connectivity.

Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self-supervised Mamba-based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real-world EHR datasets to classify the data based on the available labels and subtype patients based on the representations learned from the model. Through an extensive set of experiments, we demonstrate that our model's design choices lead to better performance compared to competitive baseline models for prediction. Moreover, we evaluate several clustering techniques to demonstrate that our findings offer valuable insights into subtyping patients based on temporal records from EHR models\footnote{Our implementations are available at https://github.com/healthylaife/triplet_mamba.

Since the first robot-assisted surgery was performed, over 40 years ago, major advances in robotics, computer vision and artificial intelligence have fundamentally changed medicine and healthcare. Innovative new technologies are already aiding skilled medical professionals in diagnosis, surgery, rehabilitation and beyond. But many questions remain: What ethical issues arise as medical tools become increasingly autonomous? How do we regulate technologies that can learn and change over time? And how can we ensure that cutting-edge medical devices are accessible to all?

A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies using designed experiments have identified multiple category-selective regions; however, these approaches rely on preconceived hypotheses about categories. Subsequent data-driven discovery methods have sought to address this limitation but are often limited by simple, typically linear encoding models. We propose an in silico approach for data-driven discovery of novel category-selectivity hypotheses based on an encoder-decoder transformer model. The architecture incorporates a brain-region to image-feature cross-attention mechanism, enabling nonlinear mappings between high-dimensional deep network features and semantic patterns encoded in the brain activity. We further introduce a method to characterize the selectivity of individual parcels by leveraging diffusion-based image generative models and large-scale datasets to synthesize and select images that maximally activate each parcel. Our approach reveals regions with complex, compositional selectivity involving diverse semantic concepts, which we validate in silico both within and across subjects. Using a brain encoder as a "digital twin" offers a powerful, data-driven framework for generating and testing hypotheses about visual selectivity in the human brain--hypotheses that can guide future fMRI experiments.

Learning efficient visual representations across heterogeneous unlabeled datasets remains a central challenge in federated learning. Effective federated representations require features that are jointly informative across clients while disentangling clientspecific factors without supervision. We thus introduce FORLA, a novel framework for federated object-centric representation learning and feature adaptation using unsupervised slot attention. At the core of our method is a shared feature adapter, trained collaboratively across clients to adapt features from foundation models, and a shared slot attention module that learns to reconstruct the adapted features.