NHS England said it had'strict policies in place for managing access to patient data'. NHS England said it had'strict policies in place for managing access to patient data'. Palantir's access to identifiable NHS England patient data is'dangerous', MPs say Health service has given US tech firm'unlimited access' to certain data to build integrated platform, according to reports Mon 11 May 2026 08.01 EDTLast modified on Mon 11 May 2026 10.06 EDT MPs have warned that an NHS decision to grant Palantir access to identifiable patient information in its plan to use AI to improve the health service is "dangerous" and will fuel public fears that data privacy is not being prioritised. NHS England has allowed staff from the US tech firm and other contractors to access patient data before it has been pseudonymised, despite internal fears of a "risk of loss of public confidence", the Financial Times reported. The health service made the move to allow Palantir to access the data in recent weeks according to the reports, which revealed an internal NHS briefing that said it would allow "unlimited access to non-NHSE staff" to part of the NHS's federated data platform (FDP), which holds identifiable patient information.

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.

Generating multivariate time series is a promising approach for sharing sensitive data in many medical, financial, and IoT applications. A common type of multivariate time series originates from a single source such as the biometric measurements from a medical patient. This leads to complex dynamical patterns between individual time series that are hard to learn by typical generation models such as GANs. There is valuable information in those patterns that machine learning models can use to better classify, predict or perform other downstream tasks. We propose a novel framework that takes time series' common origin into account and favors channel/feature relationships preservation. The two key points of our method are: 1) the individual time series are generated from a common point in latent space and 2) a central discriminator favors the preservation of inter-channel/feature dynamics. We demonstrate empirically that our method helps preserve channel/feature correlations and that our synthetic data performs very well in downstream tasks with medical and financial data.

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.

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.

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