Healthcare

Also, PUC prepares for data centers and the Texas Medical Association calls for legislative action on prediction market apps.

Despite recent advances in large language models (LLMs), their ability to generate empathetic mental health counseling responses in low-resource languages remains largely unexplored. To address this gap, we curate 625 authentic mental health cases from three complementary sources: (1) publicly available Facebook posts discussing mental health concerns, (2) transcripts from the Bangladeshi television program "Ami Akhon Ki Korbo", and (3) anonymized student questionnaire responses covering diverse

Investors navigated everything from escalating tensions in the Middle East to key tech earnings reports to healthcare developments.

Durante más de sesenta años, los astronautas solo han tenido una herramienta para diagnosticar posibles lesiones en órbita: el ecógrafo. Ahora, por primera vez, una tripulación ha conseguido tomar radiografías de diagnóstico de sus propios cuerpos mientras orbitaban la Tierra, un hito que cambia por completo lo que será posible en futuras misiones a la Luna y Marte. Te contamos todos los detalles. Por qué es importante. Hasta ahora, si un astronauta sufría una fractura o una lesión interna en el

When it comes to driving, most of us want to get to our destination as quickly (and safely) as possible. After all, given how bad sitting behind the wheel is for your health , who wants to be in a car any longer than they have to be? Most of us might expect that, when it comes to getting directions, the world’s two biggest mapping apps, Google Maps and Apple Maps , would show us the route that gets us there as fast as possible. But that’s not always the case. Here’s how to make Google Maps and A

Biomni can analyze mountains of medical data, spot patterns humans might miss, and even design experiments—helping researchers make discoveries faster in the race to cure disease.

HBO Max wants it all. The most-nominated network enters Phase 2 of the Emmy race with the year’s two most-nominated series, giving the platform a rare opportunity to capture television’s top drama and comedy prizes, and potentially make history across three major program categories. “The Pitt,” the sophomore season of the breakout medical drama, leads […]

Background: Previous research has shown that mobile health (mHealth) interventions are effective in reminding older adults with chronic conditions about health care appointments and promoting adherence to medication schedules. However, the evidence is limited by the short duration and poor quality of the interventions. Objective: We evaluated the effectiveness of the Support, Monitoring and Reminder Technology for Mild Dementia (SMART4MD) tablet app in improving the quality of life (QoL) of peop

Background: Large language models (LLMs) are increasingly used in health care, with emerging applications in clinical decision support and nursing education. However, evidence on their performance in nursing contexts, particularly in oncology nursing, remains limited. Given the complexity and high-risk nature of oncology care, it is important to evaluate the performance and clinical relevance of LLM-generated responses in oncology nursing contexts. Objective: This study aimed to compare the perf

Background: Orthopedic-related rare diseases are difficult to diagnose because of their low prevalence, heterogeneous phenotypes, and fragmented knowledge. Large language models (LLMs) can serve as dynamic knowledge-support tools, but their diagnostic performance and effect on physicians’ decision-making remain unclear. Objective: This study aims to compare the diagnostic performance of advanced LLMs for orthopedic-related rare diseases and to evaluate the effect of a 2-stage LLM-assisted diagno

To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is closely linked to the explainability of AI systems. The rapid advancements in AI across various domains have underscored the challenges of establishing trust, raising increasing interest in AI explainability even more wh

A recent uptick in lag and disconnects led me to reevaluate my home office setup. Here's how I validated my solution.

Diagnosing the root cause of anomalies is essential for safe industrial operation. Despite extensive sensor instrumentation, formulating hypotheses and gathering evidence remains a manual process, creating a major operational bottleneck. While existing data-driven approaches aim to automate this, two critical limitations restrict their deployment: their operate as black boxes unable to justify their diagnosis, and they require scarce labeled examples of faulty operation. To address this gap, we

Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way. We present a framework for conditioning such models on auxiliary clinical modalities, including ECG waveforms, chest X-ray images, and clinical notes, using modality-specific latent compression and gated cross-attention with temporal alignment. We investig

Today's links AI solipsists and AI cynics: You don't have to believe that AI "works" to believe that it's a good investment. Hey look at this: Delights to delectate. Object permanence: Malware v stolen computer; Industrial material made from old coffee and melted cups; Austro-Germany's weird tunnels; Health data v Silicon Valley; Amusement parks v load-balancing. Upcoming appearances: Edinburgh, Sydney, Melbourne, Brighton, London, South Bend. Recent appearances: Where I've been. Latest books: Y

LLM-based database agents show promise, but differing task scopes, testbeds, and metrics hinder comparison. We identify four gaps between evaluation and production operations: live-environment fidelity (multi-turn read-write interaction with a running database); observation-space scale and complexity (causal diagnosis across thousands of time series, business logs, and concurrent activity); solution-space openness (multiple remediations with different operational trade-offs); and scenario comple

Industrial health management increasingly relies on heterogeneous information sources, including condition monitoring systems, supervisory control and data acquisition systems, maintenance records, inspection results, and prognostic models. Although large language models provide new opportunities for cross-source reasoning, industrial data and analytical outputs differ substantially in structure, temporal resolution, physical meaning, and reliability. Directly integrating such heterogeneous info

Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein. Despite their therapeutic potential, computational design of molecular glues remains largely unexplored. Unlike conventional structure-based drug design, molecular glue design is governed by the unknown protein-protein interface and requires the simultaneous modeling of ligand generation, protein-protein docking,

Interactive virtual patients driven by large language models (LLMs) offer scalable solutions for medical communication training, such as breaking bad news. However, designing their emotional expressiveness remains a challenge. This paper presents an AI-driven virtual patient framework combining LLM dialogue with real-time facial animation in virtual reality (VR). We conducted an exploratory, formative evaluation with seven medical experts to gather early feedback and elicit design requirements.

The contract adds to existing Salesforce deployments for suicide prevention, health care communications and more.

AI methods promise to support autism spectrum condition (ASC) diagnostics in adults, a complex and time-consuming process, that is characterized by a shortage of specialized clinicians. To date, clinicians' needs and their interaction with such AI-based support remain underexplored. Our work aims to develop and evaluate an AI-based clinical decision support system (CDSS) for ASC assessment, and to investigate how it impacts clinicians' decision-making. By interviewing clinicians of varying exper

Automating radiology report generation is important for improving reporting consistency and clinical workflows . While Contrastive Language--Image Pretraining (CLIP) has advanced medical vision language modeling, existing CLIP-style approaches may still provide insufficient fine-grained semantic supervision for complex report generation. Standard CLIP primarily optimizes cross-modal alignment, without explicitly structuring the textual embedding space that guides visual representation learning.

Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their reactions. Beyond predictive performance, understanding how these models organize chemical information internally in their latent spaces, i.e., the embeddings of the molecules, is critical. Analyzing latent spaces helps diagnose model behavior and assess whether the learned embeddings are organized in ways that reflect mean

In this article, we investigate influences of social positionality, designer bias and educational exposure on algorithmic design in healthcare. Against the backdrop of literature on designer bias that points to how it contributes to disproportionate, discriminatory and unethical impacts for racialized and gendered bodies, this study tests this argument with an experiential case study involving Aldebaran’s NAO robot and mechatronics and robotics engineering students at a university in S. E. Ontar

Social media sentiment analysis has become one of the most significant instruments for understanding the opinion of the population in the spheres of healthcare, politics, and education. Yet, large language models (LLMs) remain unevenly distributed in their linguistic coverage, failing to adequately serve a large portion of the world's languages. This study evaluates five state-of-the-art LLMs: GPT-4o, Gemini 2.0 Flash, DeepSeek-V3, Mistral Large, and Claude 3.7 Sonnet on three-class sentiment cl

In a six-patient trial, a new gene-silencing drug reduced disease-related biomarkers, with no serious adverse events reported.

Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data. This makes them especially attractive for auditing models deployed in sensitive domains such as healthcare or finance. For these protocols to be meaningful in real-world audit settings, though, their guarantees must reflect how the model will behave once deployed, rather than merely certifying its behavior during a

Background: SMS text message reminders have been used to promote many health behaviors, such as improving diet and physical activity, managing chronic health conditions, reminding patients about medical appointments, and supporting medication adherence across a range of health conditions. Despite their promise, developing effective reminders tailored to specific patient populations is resource-intensive. AI may facilitate item development, and online research panels may provide an efficient way

Background: Molecular Tumor Boards (MTBs) generate highly technical recommendations. The language used in their protocols is rarely accessible to patients. Lay-language patient protocols could support patient-clinician communication, yet manual production is difficult to sustain in high-volume oncology settings. Large language models (LLMs) may offer scalable drafting assistance, yet clinical usability remains largely uninvestigated under real-world deployment constraints. Existing evaluations r

OpenAI is rolling out "Health in ChatGPT" to U.S. users, connecting Apple Health, medical records, and wellness apps. More than 300 million people already ask ChatGPT health questions every week, but paying users get better answers. The more powerful GPT-5.6 Sol model is reserved for premium subscribers, while free users are stuck with the weaker GPT-5.5 Instant. The article ChatGPT will give you worse health advice if you don't pay appeared first on The Decoder .

Reader’s react to the New York mayor’s video criticizing the Israeli prime minister. Also: A.I. jobs for philosophers; a battle in the health care industry; fast walkers; pregnant bellies.

An investigation by Science and Retraction Watch has uncovered details about a clinical trial that resulted in the death of its sole patient: a 6-year-old girl with a rare genetic mutation affecting her cognitive development. Her death has never been reported publicly, even though the medical team published its preclinical work in Nature earlier this … Continue reading Exclusive: A couple paid more than $800,000 for a gene-editing therapy for their daughter. She died, and it wasn’t made public

Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce \textit{MedGame}, a framework that transforms static clinical cases into structured, executable storytelling games. MedGame uses a dual-engine design: a Medical Narrative Designer synthesizes case-grounded clinical stor

OpenAI is rolling out ChatGPT Health to everyone in the US on Thursday, allowing more people to connect their medical records and health-tracking information to the chatbot. During a briefing, Ashley Alexander, OpenAI's vice president of health product, says the company's models "are now capable of reasoning at levels that are better than clinician level." […]

Users can also integrate their personal data from services like Apple Health, Function, and MyFitnessPal.

Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clinical adoption remains challenging due to limited generalization to operating room conditions. This difficulty arises because models are typically trained on labeled spectra collected from resected tissue samples, while they must operate on noisy, unlabeled data acquired directly during surgery. In addition, the black-box nature of deep learning m

Every soccer season, athletes like me are taught to watch for headaches, dizziness and blurred vision after a concussion. But no one has ever taught many of us that a concussion can also affect our mental health. Like millions of young athletes nationwide, I have played sports since I was 4 years old. Soccer has […]

Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding. However, gene expression data acquisition remains constrained by high costs and privacy concerns, limiting its use in multimodal research and AI-driven applications. We present MultiModal Molecular Generation (M$^3$-Gen), a novel framework for the generation of gene expression profiles by conditioning a Generative Adversarial N