Healthcare

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…

Microsoft has also been accused of immediately cutting off access to group benefits health insurance by a group of Bethesda Montreal employees.

What GAO Found The Office of Isotope Research and Development and Production (IRP), within the Department of Energy’s (DOE) Office of Science, produced, sold, and distributed 265 isotopes during fiscal years 2020 through 2025. Many of these isotopes are critical to medical diagnosis and treatment, national security, industrial processes and manufacturing, and quantum science. IRP made over 7,700 isotope shipments, most of which were for medical purposes. Department of Energy Isotope Production F

Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning. However, network components on quantum hardware impose fundamental limitations, while the scalability of quantum circuits leads to trainability issues. In this work, we investigate whether small, classically-emulated quantum circuit components can play a meaningful role within complex models, offering an alternative to purely classical convolutio

What GAO Found Twenty programs, supporting a broad range of services from health care to disaster assistance, made up nearly 90 percent of federal obligations among programs administered by state and other government entities with obligations of over $100 million in fiscal year 2025. The 20 programs collectively accounted for $1.1 trillion in total federal obligations that year. Subrecipients, contractors, and others can also be involved in these programs, which can be helpful in delivering bene

As Video Large Language Models are increasingly deployed in real-world applications, ensuring their safety alignment has become critical. Counterintuitively, we find that harmful videos paired with benign queries achieve higher attack success rates than the same videos paired with explicitly harmful queries. To understand the underlying mechanism of this vulnerability, we present V-DEAL, a three-level diagnostic framework that jointly analyzes this failure across model behaviour, understanding,

Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, particularly for type 1 diabetes. The lack of standardized preprocessing workflows and evaluation protocols hinders reproducibility and complicates fair comparison across studies. These challenges are further exacerbated by data-sharing restrictions, as privacy and licensing constraints often prevent the redistribution of preprocessed medical datasets. T

Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized. However, explainability of the DL frameworks remains a major bottleneck for clinical adoption, particularly when model decisions are not linked to retinal regions that are clinically meaningful. To address this issue, thi

Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisation. The emergence of LLM agents creates a new opportunity for Personal Health Agentic Analysis, where health insights can be generated adaptively and in context. However, currently there is no open-source locally deployable platform capable of processing personal health data in real time while preserving privacy. We present HiMe, a locally deplo

Assured Health raised $19 million for its AI agents that verify provider credentials and manage insurance enrollment for health systems and group practices. The startup said it can cut a process that usually takes months down to days. The post Assured Health Secures $19M to Get Providers In-Network Faster with Agentic AI appeared first on MedCity News .

arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A prior architectural study by the same authors (Tertulino and Alencar, 2026) demonstrated, on a synthetic six-feature benchmark, that server-side adaptive optimization acts as a temporal denoiser for Differential Privacy noise, answ

arXiv:2607.19827v1 Announce Type: cross Abstract: Ensuring safety in Physical AI systems operating in real-world environments is a critical challenge, particularly in hospital wards where vulnerable patients, clinical staff, medical devices, and assistive robots coexist. In this paper, we reinterpret Clinical Pathways as explicit runtime safety specifications for embodied medical AI. We propose a conceptual robotic architecture that integrates wearable sensors, smart medical devices, and assisti

Model merging combines independently trained or fine-tuned models, but pairwise alignability does not imply globally consistent alignment. We formulate merging as a finite descent problem in which checkpoints are local objects, alignment maps are transitions, and cycle products are residuals. TwistedMerge is a conservative certification pipeline that separates fixed-chart averaging, synchronization-removable gauge inconsistency, a certified central obstruction on a specified comparison complex,

The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs). Existing CNN+Grad-CAM+multimodal LLM frameworks can generate ECG reports, but their explanations are often only weakly grounded in established diagnostic criteria, reducing trust- worthiness and reproducibility. We propose a guide-grounded multimodal framework that explic

Health in ChatGPT now lets eligible U.S. users securely connect medical records and Apple Health to get more personalized insights and better understand their health.

BackgroundVibe coding—generating software through natural-language prompts to large language models without reviewing the underlying code—has moved rapidly from consumer technology into peer-reviewed clinical applications. By early 2026, clinicians had published vibe-coded teaching tools, a validated clinical nomogram, and an end-to-end omics platform built in under 10 minutes for under two dollars. Collins Dictionary named vibe coding its 2025 Word of the Year. No governance framework currently

Photovoltaic (PV) systems have proven themselves to be a viable alternative energy source; however, there are multiple faults related to PV systems which cause energy losses and low efficiencies. Manual or rule-based algorithms are traditionally used for fault diagnosis, which are not efficient and unsuitable for real-time applications. In this paper, a novel hybrid intelligent classification system for PV fault detection is proposed by integrating Fuzzy C-Means (FCM) clustering and Deep Learnin

A minor who sued multiple social media companies over claims that he suffered mental health problems due to his use of their platforms has dropped his lawsuit against Meta, just days before the case was set to go to trial in California. The plaintiff identified only as “R.K.C.” had brought claims against YouTube, Meta, Snap...

Background: Research on transgender people’s health, particularly in reproductive health, has expanded exponentially over the past decades. However, previous studies frequently highlight perceived inaccessibility and gender-exclusivity of reproductive and perinatal care for transgender people. Objective: This observational study used a cross-sectional content analysis to examine online obstetrics, labor and delivery, and pregnancy-related materials from a purposive sample of 178 online hospital

Background: Chronic low back pain (CLBP) is a major global health challenge. While nonpharmacological therapies are recommended, patient compliance is often hindered by kinesiophobia. Virtual reality (VR) offers an immersive, distraction-based approach, but the comparative effectiveness of different VR modalities remains unclear. Objective: The aim of the study is to compare and rank the efficacy of different VR-based training modalities on pain intensity, disability, and kinesiophobia in patien

Concept Bottleneck Models provide interpretable-by-design predictions by mediating diagnosis through human-understandable concepts, but in medical imaging, their trustworthiness is often limited by the quality and granularity of available supervision. In particular, predicted concept activations can be driven by irrelevant regions, leading to spatially unfaithful explanations. We study a data-centric spatially grounded Concept Bottleneck Model (SG-CBM) that leverages coarse lesion delineations a

Background: AI shows substantial potential in health care; however, the absence of standardized evaluation frameworks limits its safe and effective clinical implementation because of inconsistent validation requirements and fragmented ethical principles. Existing guidelines vary in structure, methodological rigor, and ethical integration, creating uncertainty. Objective: This study aimed to systematically map, characterize, and critically analyze existing evaluation frameworks for clinical AI, f

ChatGPT allegedly offered "extremely dangerous medical recommendations" regarding a pulmonary embolism.

There have been dozens of attacks of health care providers amid an ongoing Ebola crisis in Congo. It’s expected to increase the death rate in what’s already one of the fastest-growing outbreaks.

The case appears to be the first to argue that a chatbot’s advice harmed someone seeking guidance about a medical condition.

Clinical biomarker workflows in translational research settings often rely on spreadsheet-driven tracking, manual quality control (QC) reconciliation, and loosely integrated systems, resulting in limited state visibility, delayed reporting, and increased operational risk. These challenges are particularly pronounced in multi-day assays such as Luminex-based quantification of Fragile X Messenger Ribonucleoprotein (FMRP), where HIPAA-compliant data governance, deterministic workflow progression, a

Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives execution. We measure that survival for one frozen four-qubit ZZ feature-map kernel on $N=24$ real indoor air-quality windows, reconstructed on ibm_fez (1024 shots per circuit) under baseline, dynamical decoupling alone, and gate twirling alone, each a single non-interleaved job. Every configuration returned a complete, finite, positive-semidefinite Gr

Healthcare workers in Congo have faced local resistance, unpaid labor, and physical assault during the declared fastest Ebola outbreak in history.

Defekte Speichermodule verursachen Abstürze und beschädigen Daten, sind aber schwer zu diagnostizieren. Wir geben Tipps, welche PC-Software RAM-Fehler aufdeckt.

Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similarity measures behind such claims are fragile. We present a controlled dissection across 18 image and 7 text encoders, all open-weight and run locally, spanning 7M to 27B parameters

The fungi-infused beverage isn't backed by much science. Here's what experts say about it.

Regulator urges health service to ensure communications about benefits are ‘clearly and appropriately labelled’

Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations. Existing SVR methods are optimized and validated only for clinical-range echo times (TEs), limiting their use at non-clinical TEs and making them incompatible with quantitative T2 mapping, a protocol- and center-independent biomarker of fetal brain maturation requiring HR reconstructions across multiple

Before a healthcare robot can be useful in the real world, it has to learn how the physical world pushes back. Anatomy varies. Instruments bend, press, slip and interact with tissue. Imaging can be noisy or incomplete. And the rare, edge scenarios developers most need to understand don’t appear on schedule. That creates one of […]

Administration will also overhaul how US government funds federal research by supporting individual scientists and AI over universities The US will spend $5bn to tackle longstanding scientific problems across multiple fields using AI , ⁠the Trump administration said in a statement on Wednesday. The agencies will use the funding to identify the root causes ⁠of chronic diseases, accelerate drug ⁠discovery and ​develop longer-lasting building materials, among other tasks, according to the statement

What GAO Found The Individual Longitudinal Exposure Record (ILER) is a web application that links service members’ and veterans’ military toxic exposures and related information from Department of Defense (DOD) and Department of Veterans Affairs (VA) databases. DOD and VA intend ILER to be a multi-purpose tool to support clinicians in providing diagnoses and treatment decisions, researchers in conducting health surveillance and epidemiological research, and Veterans Benefit Administration (VBA)

Robust out-of-the-box performance is essential for the clinical deployment of deep learning models in medical imaging. An important but underexplored factor affecting model generalisability is intensity normalisation, particularly for magnetic resonance imaging (MRI), where image intensities vary across scanners and protocols. In this study, we systematically compared seven normalisation methods and their impact on the performance of a 3D U-Net model for meniscus segmentation from knee MRI. The

UK listed law firm Gateley yesterday (22 July) announced that CEO Rod Waldie is stepping down from 1 August for health reasons, following revelations first revealed by Roll on Friday […] The post Gateley’s CEO stands down following business services cuts appeared first on Legal IT Insider .

Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs). Subtle chemical and structural errors in these inputs can compromise downstream results and hinder manual inspection. LLM advances in computational chemistry offer paths beyond predictive screening toward fine-grained diagnosis with evidence-grounded explanations. However, two challenges remain: (i) limited fine-grained attribution: MOF-specific val

Eine Übersichtsarbeit zeigt, dass nur etwa die Hälfte aller Mental Health Apps mit wissenschaftlich geprüfter Wirksamkeit noch öffentlich verfügbar ist.