NHS England is pulling its open-source software from the internet because of fears around computer-hacking AI models like Mythos. A decision by NHS England to withdraw open-source code created with UK taxpayer funds because of the risk posed by computer-hacking AI models is attracting growing backlash. Last month, Mythos, an AI created by technology firm Anthropic, was widely reported to be capable of discovering flaws in virtually any software, potentially allowing hackers to break into systems running it. NHS England has now told staff that existing and future software must be pulled from public view and kept behind closed doors by 11 May because of this risk. The decision goes against the NHS service standard, which requires that staff make any software they produce open-source so that tools can be built upon, improved and used without the need for duplicated effort.

The new'AI-powered' healthcare system appears to penalise the poorest. The new'AI-powered' healthcare system appears to penalise the poorest. An AI system used to predict how much Kenyans can afford to pay for access to healthcare, has systemically driven up costs for the poor, an investigation has found. The healthcare system being rolled out across the country, a key electoral promise of President William Ruto, was launched in October 2024 and intended to replace Kenya's decades-old national insurance system. Billed as " accelerating digital transformation ", it aimed to expand access to care to Kenya's large informal economy: the day labourers, hawkers, farmers and non-salaried workers that make up 83% of its workforce.

NHS England is hurriedly withdrawing all the software it has written from public view because of the perceived risk of hacking from cutting-edge artificial intelligence. Security experts say the move is unnecessary and counterproductive. Software produced by the National Health Service has previously been made open-source and listed on GitHub because it is created with public money. This allows other organisations to build upon it and make better services more cheaply without duplicating effort. But NHS England has issued new guidance to staff, which has been shared with, that demands existing and future software be pulled from public view and kept behind closed doors.

Nicotine pouches are revered among tech workers, who tout them as the perfect brain-boosting, productivity-jacking stimulants. Entrepreneur Garrett Campbell has a 6-mg "cool mint" Zyn tucked under his lip at all times during his mammoth 15-hour workdays, aside from when he is eating. "I was always very against nicotine," says the software company founder. The 26-year-old saw his peers using nicotine pouches at college, when they first emerged as a potential productivity-boosting hack, and considered it a "degenerate thing to do." But then all of his fellow founders started fueling themselves with nicotine pouches, of which the Philip Morris International-owned Zyn is the market leader.

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

The hazard ratio from the Cox proportional hazards model is a ubiquitous summary of treatment effect. However, when hazards are non-proportional, the hazard ratio can lose a stable causal interpretation and become study-dependent because it effectively averages time-varying effects with weights determined by follow-up and censoring. We consider the average hazard (AH) as an alternative causal estimand: a population-level person-time event rate that remains well-defined and interpretable without assuming proportional hazards. Although AH can be estimated nonparametrically and regression-style adjustments have been proposed, existing approaches do not provide a general framework for flexible, high-dimensional nuisance estimation with valid sqrt{n} inference. We address this gap by developing a semiparametric, doubly robust framework for covariate-adjusted AH. We establish pathwise differentiability of AH in the nonparametric model, derive its efficient influence function, and construct cross-fitted, debiased estimators that leverage machine learning for nuisance estimation while retaining asymptotically normal, sqrt{n}-consistent inference under mild product-rate conditions. Simulations demonstrate that the proposed estimator achieves small bias and near-nominal confidence-interval coverage across proportional and non-proportional hazards settings, including crossing-hazards regimes where Cox-based summaries can be unstable. We illustrate practical utility in comparative effectiveness research by comparing immunotherapy regimens for advanced melanoma using SEER-Medicare linked data.