The US resurgence of measles is forcing hospitals and health systems to adopt new protocols and procedures to deal with a disease they thought was in the past. John Goldman had not seen a case of measles in 30 years. But since April, the infectious disease specialist with the University of Pittsburgh Medical Center in central Pennsylvania has had dozens of patients come down with the disease. "I never thought I would see this come back," he says. His health system, like others, has had to adapt to a new normal as measles makes a comeback in the United States.

The practice of editing the genes of embryos remains highly controversial and risky, but Origin Genomics founder Cathy Tie argues its a "moral imperative" to address hereditary diseases. When the Chinese scientist He Jiankui announced in 2018 that he had created the first gene-edited babies, the experiment was widely condemned as reckless and premature. It ended with He in prison. But that hasn't stopped the push for gene-edited human embryos, with biotech entrepreneur Cathy Tie arguing that doing so isn't just urgent--it's a "moral imperative." Tie is the 30-year-old founder of Origin Genomics, a company that launched in March with plans to bring gene-edited embryos to IVF clinics.

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Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Dr. Robert Waldinger didn't believe it at first. The idea that loneliness could break down the body--that the quality of your relationships might help decide whether you get coronary artery disease, arthritis, or Type 2 diabetes--struck the psychiatrist as far-fetched.

Health care company Abbott has entered into a partnership to connect Google Health to its Lingo continuous glucose monitor (CGM). According to the press release, this multi-year collaboration will allow users of the over-the-counter Lingo device to see their metabolic data within the Google Health app, which could help a person "make better lifestyle and nutrition choices in the moment." The Lingo data can also be shared with the Google Health Coach, which offers AI-driven recommendations based on an individual's information. Lingo is explicitly intended for adults who are not using insulin. Engadget reported on the growing trend of non-diabetics using continuous glucose monitors back in 2023 and found little research that supported the benefits for that population to track blood sugar spikes so closely.

Image caption, More than 2,000 images were taken of Ruth's skin I don't mind having my photo taken - triple checked and filtered for Instagram - but 70 cameras pointing at me while I'm down to my knickers is a bit daunting. A robotic voice tells me to stay still and close my eyes as I stand in a huge curved scanner while classical music plays in the background. With a flash of light, 2,000 photographs are taken of my body, in the hope of capturing every mark, freckle and mole to analyse for different skin cancers. This full-body scan is happening at a sci-fi-style clinic in Manchester city centre, with me wearing a dressing gown and hexagon-shaped rubber slippers. The millions of data points collected will create a 3D avatar of my body using AI.

The maker of Wegovy and Ozempic, Novo Nordisk, has launched legal action accusing its arch rival Eli Lilly of false advertising in suggesting its weight-loss drugs perform better. The Danish company filed a lawsuit in the US on Tuesday claiming Eli Lilly, which makes Mounjaro and Zepbound, deployed ad campaigns to create the misleading impression that Eli Lilly's medicines are superior. Novo said its rival compared the highest approved doses of its medicines for obesity and type-2 diabetes with lower doses of Novo Nordisk's, while omitting newer, higher-dose options. The BBC has contacted Eli Lilly for comment. The lawsuit comes as Novo and Eli Lilly are locked in battle to dominate the fast-growing weight-loss drug industry, especially in the US, which analysts have estimated could be worth more than $100bn by 2030.

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Event sequences appear widely in domains such as medicine, finance, and remote sensing, yet modeling them is challenging due to their heterogeneity: sequences often contain multiple event types with diverse structures--for example, electronic health records that mix discrete events like medical procedures with continuous lab measurements. Existing approaches either tokenize all entries, violating natural inductive biases, or ignore parts of the data to enforce a consistent structure. In this work, we propose a simple yet powerful Marked Temporal Point Process (MTPP) framework for modeling event sequences with flexible structure, using a single unified model. Our approach employs a single autoregressive transformer with discrete and continuous prediction heads, capable of modeling variable-length, mixed-type event sequences. The continuous head leverages an expressive normalizing flow to model continuous event attributes, avoiding the numerical integration required for inter-event times in most competing methods.

There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED(Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framework that integrates Bayesian Experimental Design (BED) with large language models (LLMs) to better emulate real-world diagnostic reasoning. At each step, ACTMED selects the test expected to yield the greatest reduction in diagnostic uncertainty for a given patient. LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data. Clinicians can remain in the loop; reviewing test suggestions, interpreting intermediate outputs, and applying clinical judgment throughout. We evaluate ACTMEDon real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use. This represents a step toward transparent, adaptive, and clinician-aligned diagnostic systems that generalize across settings with reduced reliance on domain-specific data.