Healthcare

Breastfeeding provides critical insight into infant feeding competence and physiological health, yet objective monitoring remains difficult due to the intimate and internal nature of feeding. We present Mammal, a caregiver-worn computational garment that unobtrusively monitors breastfeeding without attaching sensors to the infant. Mammal leverages inter-body signal transmission through natural mouth-to-breast contact to capture infant cardiac and feeding-related acoustic signals on the caregiver

arXiv:2607.18446v1 Announce Type: cross Abstract: Purpose: Understanding how much of routine policing involves vulnerable people could inform resourcing, training, and multi-agency response, yet administrative data provide limited insight. We explore whether an LLM-based classification pipeline, developed on open-source US police data, can be adapted to estimate the prevalence of four vulnerability indicators - mental ill health, substance misuse, alcohol dependence, and homelessness - in UK pol

arXiv:2607.19075v1 Announce Type: cross Abstract: The rapid growth of the global aging population presents severe challenges to healthcare systems, necessitating efficient, equitable, and patient-centered care models. While Industrial Engineering and Operations Research (OR) provide robust optimization and decision-support tools to address these multidimensional complexities, current applications often remain fragmented. This paper presents a thematic review of 30 seminal studies at the intersec

Support-seeker' active engagement with received comments, e.g., showing positive sentiment and willingness to improve in the replies, can indicate the success of online health communities (OHCs). Their participation in other communities may correlate with their engagement in OHCs but remains under-explored. This paper analyzes 26, 725 seekers' behaviors in the other 40, 479 communities and their associations with seekers' engagement with received comments under their 78, 501 posts in 30 Baidu Ti

Mental health disorders (e.g., depression, anxiety, post-traumatic stress disorder (PTSD), bipolar disorder) represent a pressing global challenge, and early diagnosis with continuous monitoring is critical for effective intervention. However, traditional diagnostic methods, relying on patient self-reports and clinical interviews, are subjective and often miss subtle early warning signs, a problem compounded by stigma and limited access to care. In response, recent advances in artificial intelli

IntroductionNeural Architecture Search (NAS) effectively automates Deep Learning pipeline design but is prone to validation overfitting when applied to complex tasks, such as medical image analysis. To mitigate this and enhance generalization, researchers frequently integrate Deep Ensemble Learning (DEL) and data augmentation into the NAS workflow. However, the assumption that these methodologies do not negatively interfere in high-overfitting scenarios remains unproven.MethodsWe evaluated NAS,

This retrospective study enrolled 492 patients with scrub typhus in Jiangmen from 2013 to 2025. Clinical and laboratory data were analyzed using univariate logistic regression and LASSO regression to identify risk factors for severe illness. Seven machine-learning models, including logistic regression, support vector machine, random forest, XGBoost, Naive Bayes, k-nearest neighbor, and decision tree, were constructed and externally validated. Variable importance was ranked using SHAP analysis. M

The shape of a planar curve is the geometric information that remains once translation, rotation, scale and reparametrisation are removed and is of interest in many health applications, e.g. in neuroimaging. We propose a deep shape regression model for open planar curves that admits multimodal and high-dimensional covariates. Representing curves as complex-valued functions, we show that the conditional full Procrustes mean is the leading eigenfunction of the conditional covariance. To estimate t

Background: Effective communication is crucial for high-quality health care, but systemic barriers still disrupt patient-provider interactions. Research shows that communication failures are a major reason for preventable medical errors. These issues are linked to around 30% of malpractice claims and over 1744 deaths each year in the United States. In addition, hospitals lose about US $12 billion each year because of miscommunication. Despite the critical nature of this issue, the literature rem

Background: Digital health literacy is an essential skill for processing health-related information in today’s technology-driven society. Recent literature highlights deficits in digital health literacy among adolescents and legislative initiatives to secure its promotion have been introduced (eg, in the German Social Code Book V). However, few interventions target adolescents; existing programs often overlook critical aspects like graph literacy or lack applicability in schools and rigorous sci

As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream generation. Yet existing evaluation systems remain confined to scoring documents independently and aggregating via nDCG, ignoring inter-document interactions (redundancy, conflict, complementarity) and unable to answer what makes one document set better than another. To address these issues, we propose a complete evaluate-diagnose-optimize framework

Xaira Therapeutics is all in on data generation for model building! We talk with Bo Wang and Ci Chu about how and why.

Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness. This study evaluates a federated learning fram

Background: Lifestyle interventions for patients with prostate cancer have been shown to improve treatment adherence and quality of life. However, there remains a lack of large language models (LLMs) capable of delivering individualized and professional lifestyle recommendations under clearly defined medical safety boundaries and controlled evidence sources. Objective: This study aimed to develop and evaluate a supervised fine-tuned LLM—PCaPLMM_SFT (Prostate Cancer Patient Lifestyle Management M

As Texas schools face at least $600 million in federal funding cuts, multiple mental health programs, particularly those implemented in response to the pandemic and mass shootings, are at risk of losing funding. School programs focused on chronic absenteeism, mental wellness and crisis services that were created in response to the Uvalde school shooting, as […]

Anne Lord Bailey, who leads VA’s Strategic Initiatives Lab, said veterans who have used immersive tech have reported therapeutic benefits that extend beyond the conditions being treated by the headsets.

The Variational Quantum Eigensolver (VQE) is a leading algorithm for estimating molecular ground-state energies on near-term quantum hardware, with applications spanning quantum chemistry, materials science, and drug discovery. As VQE workloads are increasingly deployed through cloud-based ``VQE-as-a-service'' pipelines, they become exposed to adversaries such as compromised service components, malicious co-tenants, or insiders in the transpilation stack, any of which can corrupt results before

Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence. However, most existing pathology benchmarks evaluate models on pre-cropped patches or pre-extracted slide features, leaving their ability to acquire evidence directly from gigapixel WSIs largely untested. We introduce PathAgentBench, a benchmark for evaluating evidence-seeking vision-language models (VLMs) across four complementary ca

States have made steady progress including students with disabilities in mainstream classrooms, an independent federal report finds, but lawmakers and advocates worry that headway will be lost as federal special education offices move from the Department of Education to Health and Human Services. Released this month, the Government Accountability Office report shows the number of […]

The mission has not succeeded in its core goals, and it’s using up military manpower and munitions at a time when the US is stretched on both, writes Daniel R. DePetris.

The report envisions a decade in which drones move goods, medical supplies, and information at a scale comparable to national infrastructure investments like highways and the electric grid. Potential applications include natural disaster detection drones that spot wildfire sources within minutes, medical supply chains that bypass ground congestion to reach rural hospitals, and nationwide fleets that continuously inspect bridges and power lines. Realizing this future, however, requires closing wh

Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot detect when a model reaches the correct diagnosis while grounding it in irrelevant, absent, or contradictory evidence. We introduce MIRA-Ev, a clinical argument mining benchmark built on Spanish Médico Interno Residente (MIR) licensing-exam cases, re-annotated by expert clinicians with span-level premises, claims, and directed support/attack relations, and re

The Health Assistant chatbot is available "in beta for eligible US users," Samsung says.

Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy. Optimizing only paired pixel fidelity can suppress uncertain lesion enhancement, whereas adversarial or stochastic generative objectives can favor realistic post-contrast appearance without guaranteeing patient-specific lesion fidelity. We introduce MIRAGE, a residual 2D U-Net that combines global r

These three legal regimes are pulling in different directions, and the developers that navigate them well will be the ones that plan for all three from the start. The post The AI Tension In Healthcare: Patent Strategy, FDA Reality, And HIPAA Constraints appeared first on Above the Law .

Background: Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly following trauma, remain incompletely understood. Machine learning could be applied to identify previously unknown patterns in large-scale, multimodal datasets. Objective: This study aims to test the ability of a new sparse group factor analysis method to uncover hidden patterns in large-scale multimodal datasets and generate testable, clinic

If the US wanted to be the world’s police officer, then why not try to be its doctor too? Just two months before the invasion of Iraq in 2003, George W. Bush announced an ambitious plan to pump $15 billion into the global fight against HIV, stunning his allies in Congress, health advocates, and heads […]

Health care digital transformation is gaining increasing attention, despite the observed challenges in its implementation. The envisioned benefits, together with the growing need for better health care, are motivating academia, organizations, regulatory agencies, and governments to develop more effective digital health care solutions. Through extensive debates among the authors, this paper discusses how digital transformation is being conducted in the health care sector. Our discussion relies on

The rapid growth of the global aging population presents severe challenges to healthcare systems, necessitating efficient, equitable, and patient-centered care models. While Industrial Engineering and Operations Research (OR) provide robust optimization and decision-support tools to address these multidimensional complexities, current applications often remain fragmented. This paper presents a thematic review of 30 seminal studies at the intersection of OR and elderly care, categorizing the lite

Objective Structured Clinical Examinations (OSCEs) are the gold standard for assessing clinical competence, yet scoring remains vulnerable to examiner subjectivity, fatigue, and cognitive bias. Standard examiner validation via inter-rater statistics lacks explanatory power regarding the source of errors, as it neither analyzes examiner reasoning nor verifies examiner claims against actual events. Thus, we introduce Quality Action Assurance (QAA), a multimodal framework that verifies examiner cla

Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design. Current methods primarily rely on supervised training or fine-tuning with limited datasets, which are insufficient to capture complex molecular design objectives. While some approaches attempt to guide generation toward specific goals, they often lack direct optimization mechanisms, making it difficult to align generated molecules with desired properties. To tackle these c

Zipline is preparing an expansion backed by a US government grant across Nigeria, evolving from state-by-state partnerships to engaging healthcare stakeholders at the local, state, and national level.

Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice. Methods: We propose an adaptive clinical intelligence architecture for ICU intervention prediction that structurally decouples physiological from treatment representations, confining parameter updates to the treatment stream upon a dual distributional a

These healthy habits benefit your whole body—including your brain—and may help contribute to lower dementia risk over time.

GAO’s work regularly finds that federal programs are unable to assess their performance to determine if they are solving the problem they were created to fix. By defining goals and collecting and using relevant data, agencies could make informed decisions to improve their programs’ results. The Big Picture Each year, the federal government spends trillions of dollars on programs that Americans depend on, such as health care, public safety, and disaster support. Our recent reports have found that

We present OntoBook, a method that converts medical ontology structure into pretraining signal for encoder language models. Our approach has three stages: random walks through ontology graphs capture hierarchical and causal relations between medical codes, a large language model reformulates these walks into fluent textbook-style prose, and the resulting text is used to train ModernCamemBERT, a 149M-parameter French encoder, with two objectives on the same data: masked language modeling and rela

A second-year medical student in Nepal is at the center of an international research network churning out everything from systematic reviews and meta-analyses to case reports and database studies, Retraction Watch has learned. The student, Raghabendra Kumar Mahato, leverages this network, which includes nearly 1,000 members connected through the WhatsApp messaging platform, to produce large … Continue reading Exclusive: Medical student in Nepal behind busy research factory

The robots are expanding into healthcare, logistics, retail, hospitality and education as AI advances fuel global demand.

Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes. However, the successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness. This study examines public perceptions of automated decision-making (ADM) in healthcare. Data were drawn from the first wave of an ongoing longitudinal survey panel. The final sample consisted o