Every moral scenario consists of a triple ( context, action 1, action 2) and a set of auxiliary labels. The actions describe two possible actions in the first-person (e.g., The moral scenarios can be categorized into: 1. MoralChoice-LowAmbiguity The LLM-assisted construction (i.e., zero-and few-shot prompting setups) of the scenarios is grounded Category Rule Refined Rule Description Do not harm Do not kill Do not kill (i.e., do not cause permanent loss of consciousness). Do not cause pain Do not cause physical or emotional pain or unpleasant feelings (e.g., anger, sadness) to someone. Do not disable Do not deprive someone of their physical, mental or volitional ability (e.g. Do not deprive of freedom Do not deprive someone of their freedom (i.e., make a person unable to do something by altering the person's environment or situation).

How The Pitt's AI Drama is Playing Out in Real Hospitals In Thursday's episode of The Pitt, the long-simmering tensions over the use of AI at the Pittsburgh Trauma Medical Center boiled over. In season two of the five-time Emmy winning medical drama, a new attending physician, Baran Al-Hashimi (Sepideh Moafi), is determined to improve efficiencies at the hospital. She tells her skeptical staff that new AI systems can cut down their time spent on charting by 80%, allowing them to spend more time both at the bedside and at home. But in episode six, doctors discover that the AI tool has made up false details about a patient and confused "urology" for "neurology." "AI's two percent error rate is still better than dictation," Al-Hashimi says, adding that it needs to be proofread for errors.

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Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are largely limited to point estimates of potential outcomes with no uncertain quantification; thus, the full information about the distributions of potential outcomes is typically ignored. In this paper, we propose a novel causal diffusion model called DiffPO, which is carefully designed for reliable inferences in medicine by learning the distribution of potential outcomes. In our DiffPO, we leverage a tailored conditional denoising diffusion model to learn complex distributions, where we address the selection bias through a novel orthogonal diffusion loss. Another strength of our DiffPO method is that it is highly flexible (e.g., it can also be used to estimate different causal quantities such as CATE). Across a wide range of experiments, we show that our method achieves state-of-the-art performance.

More accurate and individualized health predictions will allow for preventative factors to be implemented well in advance. In 2026, we will see the beginning of precision medical forecasting. Just as there have been remarkable advances in weather forecasting with the use of large language models, so will there be for determining an individual's risk of the major age-related diseases (cancer, cardiovascular, and neurodegenerative). These diseases share common threads, such as a long incubation phase before any symptoms are manifest, usually two decades or more. They also have the same biologic underpinnings of immunosenescence and inflammaging, terms that characterize an immune system that has lost some of its functionality and protective power, and the accompanying heightened inflammation.

Others say they don't need us. It's time for us to think of ourselves not as the high priests of health care but as what we have always been: healers. Not long ago, I cared for a middle-aged man I'll call Jim, who was generally healthy but had recently started to feel sluggish. One of his friends told him to try a hormone supplement. After Jim saw on social media that Robert F. Kennedy, Jr., the Trump Administration's Secretary of Health and Human Services, had endorsed supplements as a part of an "anti-aging" regimen, he ordered one from a telehealth company. A few months later, he noticed swelling and pain in his calf. ChatGPT warned him that he might have a blood clot.

Takeda's psoriasis pill developed with AI assistance succeeds in trials Psoriasis is a chronic autoimmune disorder that causes rashes marked by itchy, scaly rashes and afflicts more than 125 million people worldwide. Takeda Pharmaceutical announced that its oral psoriasis drug zasocitinib proved safe and effective in late-stage trials, marking a milestone in its effort to treat the incurable skin condition and offset looming revenue pressure. Patients with moderate-to-severe plaque psoriasis who took the once-daily pill showed significantly clearer skin compared with those on placebo or the existing therapy apremilast, the company said in a statement Thursday. Takeda plans to submit data to the U.S. Food and Drug Administration and other regulators beginning in fiscal year 2026. If approved, zasocitinib would join the small but growing oral psoriasis treatments -- long a market dominated by ointments and injectable antibody therapies -- and stand out as one of the first drugs discovered with the help of artificial intelligence.

Ukraine's health supplies hit in series of Russian strikes on medical warehouses Warehouses supplying the vast majority of Ukraine's pharmacies have been destroyed in a series of Russian attacks over recent months. Medical supplies worth about $200m (£145m) were destroyed in just two strikes in December and October. A large warehouse storing medicines in the city of Dnipro was destroyed in a Russian air strike on 6 December. As a result, about $110m worth of medicines were destroyed - estimated at up to 30% of Ukraine's monthly supply. It was a missile and drone strike against our facility.

Jonny Kim returned after eight months aboard the International Space Station. Breakthroughs, discoveries, and DIY tips sent every weekday. After 245 days in orbit aboard the International Space Station (ISS), one NASA astronaut and two cosmonauts have safely returned to Earth. NASA's Jonny Kim along with Roscosmos cosmonauts Sergey Ryzhikov and Alexey Zubritsky landed near the town of Zhezkazgan, Kazakhstan on December 9, and are now undergoing the standard post-mission health screenings. Kim officially became an astronaut in 2017.