'This is an apartheid regime' Does Trump have real leverage over Netanyahu? Hospital workers wounded in Israeli drone attack on Gaza's Kamal Adwan An Israeli drone attack on a hospital in northern Gaza injured staff members, despite a " ceasefire " being in place, according to the Palestinian Ministry of Health. At least three people were hurt in the courtyard and three others nearby, medical sources said. The attack happened despite the facility being in the so-called Green Zone, an area under Israeli control. The ministry condemned the attack, describing it as part of Israel's "systematic targeting of health facilities".

Artificial intelligence will be used on the NHS app to determine which service is most appropriate for patients in England, the health service has announced. A new triage tool will ask patients a series of questions, and will use the responses to direct them to a GP appointment, pharmacy, A&E, community service or offer self-care advice. NHS England said the update would reach more than 200,000 patients in the next 12 months and be available to all app users by April 2028 as part of a major overhaul of its technology. The rollout has been largely welcomed, but some health bodies urged the NHS to prioritise patient safety, confidentiality and inclusion as it grows more reliant on AI. An initial trial of the tool at Wealden Ridge Medical Partnership in Sussex saw a 29% reduction in the number of people queuing on the phone for an appointment.

The app will be used to triage patients and to ascertain if they should be allocated a GP appointment. The app will be used to triage patients and to ascertain if they should be allocated a GP appointment. Sat 4 Jul 2026 17.30 EDTLast modified on Sat 4 Jul 2026 18.02 EDT The NHS will begin using AI on its app to direct patients to the appropriate services, it has been announced. The tool will be used to triage patients and to ascertain if they should be allocated a GP appointment. Some may be advised to attend a pharmacy or their local A&E department instead, depending on the severity of their condition.

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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.

Plus: The Trump administration has asked OpenAI to limit its next model release. Scientists are trying to figure out why. It's been hot in London this week. A dangerous heat wave has hit Western Europe. On Wednesday, the UK recorded its highest ever June temperature at 36.1 C (about 97 F). But as the weather app on my phone confirmed, it 39 C. Much of Western Europe is suffering, bringing awful consequences for agriculture, infrastructure, and the health system.

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Two narratives about machine learning ecosystems grew out of the recent algorithmic fairness discourse. In one, dubbed monoculture, algorithmic ecosystems tend toward homogeneity akin to a single model making all decisions. Individuals then face the risk of systematic exclusion with no recourse. In the other, model multiplicity, many models solve the same task with similar accuracy, causing excessive variation in individual outcomes. Both narratives are compelling, yet, seemingly at odds: model multiplicity can't materialize in a strict monoculture.

Accurately estimating treatment effects in time series is essential for evaluating interventions in real-world applications, especially when treatment assignment is biased by unobserved factors. In many practical settings, interventions are adopted at different times across individuals, leading to staggered treatment exposure and heterogeneous pre-treatment histories. In such cases, aggregating outcome trajectories across treated units is ill-defined, making individual treatment effect (ITE) estimation a prerequisite for reliable causal inference. We therefore study the problem of estimating the average treatment effect for the treated (ATT) by first recovering individual-level counterfactuals. We introduce a neural framework that learns simultaneously low-dimensional latent representations of individual time series and propensity scores. These estimates are then used to approximate the individual treatment effects through a flexible matching procedure that avoids classical convexity constraints commonly used in synthetic control methods. By operating at the individual level, our approach naturally accommodates staggered interventions and improves counterfactual estimation under latent bias, without relying on explicit temporal modeling assumptions. We illustrate our approach on both real-world energy consumption data and clinical time series, including high-frequency electricity demand-response programs and semi-synthetic data for individuals in intensive care unit (ICU), where hidden confounding, staggered treatment adoption, and non-stationary dynamics are prevalent.

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