Healthcare

Electroencephalography (EEG)-based emotion recognition captures affective neural signals with high temporal precision, but cross-subject variability and label noise remain critical challenges to its practical healthcare deployment. Existing label-denoising methods lack physiological grounding, while physiology-informed approaches rely on hand-crafted hyperparameters. To bridge these two paradigms, we propose PhyDA, a plug-and-play, tuning-free framework that unifies neurophysiological priors wit

Early identification of heart disease is important to reduce mortality rates and to provide timely medical intervention for better patient outcomes. In recent studies, machine learning has been used to predict cardiovascular risk, but many existing models use basic feature sets and fixed decision rules. This can limit their ability to adapt when new data are introduced and may also reduce their capability to detect early signs of risk. In this study, we present a heart disease prediction framewo

IntroductionReproductive tract infections (RTIs) and sexually transmitted infections (STIs) pose a substantial economic burden and public health concern in developing countries such as India, where inadequate early detection and prevention strategies often lead to increased morbidity, mortality, stigma, cancer and adverse reproductive health outcomes in both men and women.MethodsThe present cross-sectional study employed machine-learning models to predict the risk of RTI/STI among young women in

A blood test now helps doctors decide, in about 15 minutes, whether a patient with a suspected brain injury needs a CT scan. It took about 20 years to get there. That gap between scientific promise and clinical use tells us more about American innovation policy than most congressional hearings ever will. At one such ... A Patent, a Blood Test, and 20 Years of Waiting The post A Patent, a Blood Test, and 20 Years of Waiting appeared first on Truth on the Market .

Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods. Here, foundation models are pre-trained on mixtures of complex clinical data modalities, useful for various downstream tasks. Existing works often utilise Electronic Health Records (EHR) to provide rich and diverse patient observations to train clinical foundation models. Howe

Background: Malawi was a pioneer among African countries in implementing a coordinated, government-led effort to streamline COVID-19 support using digital health tools. In response to the pandemic, a COVID-19 WhatsApp chatbot was developed to support the public with information, symptom reporting, and service navigation during the pandemic. Objective: This study describes the national deployment, functionality, and use patterns of the WhatsApp chatbot during the COVID-19 pandemic in Malawi. Meth

Background: eHealth interventions have demonstrated potential to address challenges related to health and the health care system in low- and middle-income countries. To effectively leverage eHealth in supporting health care in Ethiopia, the assessment and development of the eHealth literacy of patients are essential. Objective: This study aimed to translate the eHealth Literacy Questionnaire (eHLQ) to Amharic and assess its psychometric properties. Methods: A systematic process of translation, i

Electronic health record audit logs record timestamped actions through which clinical work is carried out. Generated as operational metadata, they now support research on clinician effort, patient outcomes, care-team coordination, and workflow structure. This Perspective explains that breadth by articulating audit logs as multi-axial event streams and drawing implications for representation learning, evaluation, and governance. Each logged action belongs simultaneously to multiple clinically mea

The Trump administration is testing the use of AI in Medicare to approve some medical services.

Background: Standard echocardiography reports use complex terminology, limiting patient comprehension and exacerbating preconsultation anxiety. Large language models (LLMs) can transform technical data into patient-friendly narratives by incorporating longitudinal comparisons with prior examinations. Objective: This study aims to develop an LLM-based patient-friendly echocardiography reporting system and evaluate its professional safety, patient comprehension, and impact on short-term anxiety. M

Potential purchase of ArisGlobal from Swedish buyout group would boost French conglomerate’s push into life sciences

Background: Just-in-time adaptive interventions (JITAIs) use real-time data to deliver personalized support at moments of heightened need and may improve dietary behaviors in real-world settings. Objective: The aim of this study is to systematically review the application, characteristics, and effectiveness of JITAIs in dietary health management. Methods: We included human studies evaluating JITAIs-based dietary interventions delivered through digital platforms that used real-time or near–real-t

Large language models (LLMs) are increasingly embedded in clinical and population health workflows, including conversational agents such as health chatbots. As chatbots evolve from rule-based approaches to hybrid and LLM-enabled designs, risks and concerns about deployment readiness shift. Unlike rule-based chatbots, LLM outputs can be unpredictable, error-prone, and difficult to validate with traditional evaluation methods. Public health teams integrating customized LLMs into interventions face

Background: Digital technologies are becoming an important part of health care, including for individuals with attention-deficit/hyperactivity disorder (ADHD). Digital health innovations present valuable opportunities to provide flexible and tailored support for their diverse needs, along with significant challenges. Attentional, organizational, and motivational characteristics associated with ADHD may affect how individuals engage with digital tools. Potential risks include additional access ba

Researchers have restored hand movement and the sense of touch to a man paralysed from the chest down. The results, published in Nature Medicine, suggest the technology partly rewired his nervous system. The system, called a “double neural bypass,” comes from the Feinstein Institutes for Medical Research, the research arm of Northwell Health, the team […] This story continues at The Next Web

Background: Patients increasingly use large language models (LLMs) to obtain medical information, but the quality of LLM-generated information on complex spinal infections such as spondylodiscitis remains uncertain. Existing evaluations in spine surgery have mainly addressed degenerative conditions or surgical procedures, and disease-specific data for spondylodiscitis are limited. Objective: This preliminary study evaluated spine surgeons’ ratings of single-turn LLM responses to 10 author-curate

Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability. We propose a self-evolving, ex

AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate model capabilities, rank systems, select informative examples, and diagnose benchmark quality. However, AI benchmark data often departs from the data regime of human testing, for which standard IRT estimation tools were originally developed: benchmarks typically involve fewer evaluated models, far more items, and capability distributions that may be skewed, clustered, or multim

Most medical AI benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? We present a clinician-built synthetic benchmark and failure atlas that labels medical AI errors by severity (1--5) and safety gate type (missed urgent escalation, unsafe remote dosing, unsafe discharge reassurance, evidence fabrication, unsafe protocol execution, source support gap). The current public release (v0.2.1) contains 44 clinician-reviewed

World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, while the world models themselves are rarely studied in isolation. We examine five representative visual world-model agents in Atari Pong: DreamerV3, DIAMOND, TWISTER, Simulus, and STORM. After reproducing their training pipelines and matching the reported agent performance, we freeze the learned world models and evaluate them with a closed-loop rollout diagnostic: a policy trained separately f

A group of researchers at Brigham and Women’s Hospital and Harvard Medical School in Boston have lost a paper for image duplication following an investigation by the two institutions. The paper, published in September 2019 in the Journal of Experimental Medicine, described a treatment for tumors caused by a disorder called tuberous sclerosis complex. Several … Continue reading Harvard cancer researchers earn retraction for image duplication

Patient went from not being able to lift his hands to his face to independently wiping his mouth

Daniel Solove argues in the Wall Street Journal (alternate link ) that giving people control of their personal data is not an effective way to regulate privacy in this era. Instead, we need to hold companies accountable for their actions, similar to what we do with food and drug companies. Measures such as rigorous data minimization, fiduciary duties, liability for negligent or reckless technological design, liability for algorithms that cause harm, and multi-stakeholder review of technologies w

Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries. Existing methodologies attempt to bypass these constraints using computationally expensive, fully fine-tuned hybrid architectures that relegate spatial explainability to a post-hoc approximation

The Coalition Against Surprise Medical Billing launched a campaign opposing the No Surprises Act Enforcement Act, arguing it would worsen IDR process abuse. The post Coalition Urges Policymakers To Reject No Surprises Act Enforcement Bill appeared first on Above the Law .

On 15 July 2026, CDT Europe and other 5 organisations representing standardisation, consumers, digital rights, doctors, pharmacists and hospitals published an open joint letter calling EU policymakers to maintain medical devices under the scope of the AI Act. In the letter, we express our serious concerns regarding the European Commission’s proposal to exclude medical devices […] The post Open Joint Letter on the AI Act Regulating AI-embedded Medical Devices appeared first on Center for Democrac

Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in healthcare. It works by minimizing the distance between matched (positive) data modalities, while maximizing the distance between mismatched (negative) samples. Traditional CL frameworks typically assume instance-based correspondence within data batches, treating all non-paired samples as negatives. However, this assumptio

Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect. We argue that a demographically-conditioned synthetic generator can do both: mitigate bias on the training side and detect bias on the evaluation side. Working on COVID-19 chest CT classification with an end-to-end fine-tuned Stabl

A retired judge advocate explains how a future administration could decide who to prosecute for the illegal strikes on alleged drug-trafficking boats. The post How to Choose Which Military Members to Hold Accountable for Illegal Boat Strikes appeared first on Just Security .

Why This Matters U.S. clinicians average a 57-hour workweek, including 7 hours of administrative work. Time spent on tasks like drafting patient visit notes or reviewing billing paperwork may contribute to clinician burnout. New AI tools could increase efficiency and reduce administrative burdens during and after patient visits. Key Takeaways Some health care providers are adopting AI tools to assist with note taking and medical coding, which may save time and reduce burnout. The accuracy of the

Collection of expert analysis on the legality of the U.S. strike on Venezuelan vessels in the Caribbean, the consequences of the strike, and related issues. The post Collection: U.S. Lethal Strikes on Suspected Drug Traffickers, Operation Southern Spear, Operation Absolute Resolve appeared first on Just Security .

Therapy and mental health wellness work best when they are treated as a daily routine, not a weekly appointment, writes Joan M. Cook.

CMS wants to build a standardized payment structure for clinical software and AI that factors in their impact on patient outcomes.

Das GeDIG-Gesetz erzeugt gemischte Reaktionen: Ärzte warnen vor Vertrauensverlust, die Digitalwirtschaft fordert unter anderem mehr Rechte bei der Datennutzung.

Machine learning (ML) models deployed in sensitive domains such as healthcare, law enforcement, and finance must satisfy not only utility requirements but also fairness and privacy guarantees. While prior work has largely examined how privacy-preserving techniques affect fairness, the inverse question-how fairness-enhancing algorithms influence privacy leakage-remains underexplored. We present the first comprehensive study of how fairness interventions affect membership inference privacy risks a

She has studied adolescent mental health for 25 years and fears the debate obscures some of the biggest issues facing teenagers – from the impact of Covid to the health of their adult caregivers The quickest way to make being online safer for children and teens would be to kick all adult men off the internet, the Canadian psychologist Candice Odgers believes. Men are the biggest perpetrators of sextortion and most likely to spread misinformation, she says. Odgers is not recommending this as a po

arXiv:2607.13038v1 Announce Type: new Abstract: We present the design and implementation of a safety constrained large language model (LLM) system for public health information access, focusing on maternal and child health (MCH) resource navigation. While LLM based systems offer flexible and natural interfaces for information retrieval, their deployment in healthcare contexts introduces risks related to safety, trust, and uncontrolled generation. This work explores practical design patterns for

arXiv:2607.13914v1 Announce Type: cross Abstract: This paper presents a holistic systems informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of established industries such as manufacturing, logistics, services and beyond, there is a dearth of concentrated review and application of the data-driven DM

Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines. However, existing approaches are typically limited to single-modality modeling, which restricts their generalization in complex scenarios. Although recent advances in large language models (LLMs) provide new opportunities for multimodal learning, bridging continuous time-series signals and discrete textual semantics remains