Healthcare

Medical ethics often turns on categories. A patient is disabled, a person is a parent, an entity is an embryo, a participant is vulnerable. These descriptions can identify features of a case that have ethical or legal significance. They can also carry assumptions that have not been fully argued for: for example, about moral status, decision-making authority or susceptibility to harm. 1 This risk is greatest in marginal or difficult cases, where new clinical, legal or scientific contexts stretch

The pace of scientific advancements in medicine, driven by artificial intelligence as much as by novel biotechnologies, demands an ever-faster update of professional knowledge from physicians and collaboration in interdisciplinary teams. At the same time, the increased heterogeneity of patients’ lifeworlds in socially and culturally diverse societies requires healthcare professionals to consider diverging personal and cultural perspectives in their treatment recommendations. Both developme

Predictive tools made possible by advances in machine learning techniques may help clinicians make more accurate decisions about who should be allocated costly therapies, such as immunotherapy, which only work on a relatively low proportion of patients. In this article, I argue that a fair decision procedure must recognise each patients’ chance of responding well. To do so, the procedure should not apply a fixed threshold to probability scores. Rather, each patient should be given a chance

Decisions for patients in the neonatal intensive care unit (NICU) are made under the auspices of the shared decision-making model, which uses the best interests standard as a guide. Decisions made regarding the withdrawal of life-sustaining measures (WLSM) are also made using the shared decision-making model with attention to either physiological parameters indicative of survival or the potential for disability. The two dominant frameworks for considering disability are the medical and social mo

Background Toomey et al (2024) found that US participants were more likely to follow a medical treatment preference—expressed after substantial cognitive decline—of a third person rather than their own future self. This correlated with a greater tendency to see the third person as still their true self. We hypothesised that the greater epistemic access one has to one’s own true self as opposed to others might drive this difference. Methods A codebook designed to capture differe

Effective public health responses to many infectious diseases require sustained collective action. Communicable disease control in populations can only be achieved by high levels of public compliance with health directives. However, governing authorities have limited options if public compliance is insufficient and collective action is failing. Mechanisms to promote public compliance occur on a spectrum from providing public health advice, offering incentives so people cooperate more, to enactin

Following years of debate over the effectiveness of cognitive behavioural therapy for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), public health bodies in the UK and beyond have determined that no psychotherapy is clinically proven for this patient group. In the field of ME/CFS and the wider arena of ‘medically unexplained symptoms’ (MUS), patient survey data and qualitative research capturing patient experiences and psychotherapist attitudes suggest that therapeutic

Healthcare resource allocation decisions for high-cost drugs for rare diseases (DRDs) raise several challenges for decision makers, and, given the complexity of the decisions and the limited funding available for DRDs, it is reasonable to anticipate indeterminacy arising about which DRDs to fund. We argue that when indeterminacy does arise, one might consider resolving it by using a lottery. We examine the extent to which a lottery and the commonly used process of first come, first served satisf

This paper explores resource allocation complexities during health emergencies, focusing on pervasive racial disparities, notably affecting black communities. It aims to investigate alternatives to the Most Lives Saved approach, particularly its potential to exacerbate disparities. To analyse resource allocation strategies, the essay reviews the Dual-Principled System proposed by Bruce and Tallman (B+T) in 2021. B+T’s proposal critiques previous methods like the Area Deprivation Index and

We respond to Gillon’s critique of our data-driven analysis of the history of Journal of Medical Ethics ( JME ), in which we used a topic model to trace intellectual trends in the journal’s first 50 years. Gillon, drawing on his personal memories as JME ’s second (and longest serving) editor, challenges several of our findings, particularly those concerning the prominence and classification of topics such as Ethics education . In this reply, we clarify misunderstandings that le

The first section provides the standard understanding of the concept of ‘đong bào’ (hereinafter ‘đong bào’). This understanding is widely shared by the Vietnamese populace for it to stand robustly and independently from any controversies. Then, implications of ‘đong bào’ for two areas of bioethics—transplantation and ethics in crises—are provided. Finally, ‘đong bào’ shall be developed for the purpose of glob

Why the Llama lead left Meta for drug discovery, PEARL's zero-shot OpenBind win, and what becomes possible when co-folding finally crosses the accuracy threshold.

NVIDIA and its partners are investing in American manufacturing, supply chains, energy grids and skilled workforces so the U.S. can produce the infrastructure needed for better healthcare, breakthrough scientific discovery, stronger industrial productivity and global technology leadership.

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning. Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare

Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks succeed and informs the design of robust alignment strategies. Prior work shows that aligned LLMs encode harmfulness and refusal as separable directions in the residual stream at prompt-side token positions. We show that jailbreaks succeed at prompt encoding by suppressing either the refusal or harmfulness direction before any token is generated, with dis

The intersection of AI, healthcare, and visualization is evolving rapidly, posing challenges that cut across disciplinary boundaries and resist easy resolution. The Visual Analytics in Healthcare workshop (VAHC), co-located every other year at the IEEE VIS conference and the AMIA (American Medical Informatics Association) annual conference, has served as a forum to connect the visualization and medical informatics community since 2010. In 2025, to celebrate the 16th edition, we used the workshop

The research proposes a multilayer Q-matrix-embedded neural network for cognitive diagnosis (M-QCDNet), which integrates the structural interpretability of cognitive diagnostic models (CDMs) with the deep learning neural network (NN). M-QCDNet structures the item-skill relationship using the Q-matrix as a structural prior, ensuring latent mastery profiles remain interpretable and consistent with cognitive theory, followed by the proposed loss function with an L2 penalty to penalize skills not al

The implementation of artificial intelligence (AI) in radiology has garnered significant attention across both research and practice over the last decade. However, opinions on whether AI will augment or replace radiologists in their work have so far been divided, and the debate around this continues to linger. This article conceptually structures and terms this phenomenon as the “replacement-augmentation paradox” of AI in radiology—the persistent coexistence of replacement fears and augmentation

As more Chinese turn to AI tools for counseling, mental health professionals warn that the technology could be making people worse.

The European Commission wants to treat cloud computing as a gatekeeper market. That is the wrong diagnosis, and it would lead to the wrong cure. The Commission’s preliminary view that Amazon Web Services (AWS) and Microsoft Azure should be designated as Digital Markets Act (DMA) gatekeepers for cloud-computing services is more than another skirmish in ... Brussels Goes Gate-Hunting: AWS, Azure, and the DMA’s Cloud Problem The post Brussels Goes Gate-Hunting: AWS, Azure, and the DMA’s Cloud Probl

We hear concerns that shadow banning is limiting access to health advice for women.

Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision, where fixed prompts, rules, or reward models provide non-adaptive guidance throughout training. Such static signals are often sufficient to enforce output formats, but fail to shape the underlying reasoning process, leading to brittle generalization and performance saturation in complex decision-making tasks. We propose Evo-PI, a principle-centri

As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behaviors are genuine or superficial. We show that current fairness evaluations substantially overestimate moral safety. Models appear fair when demographic identity is stated as an explicit label, yet become measurably less fair when the same identity must be inferred. We term this failure performative compliance, where a model is fair when the present

Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many Machine Learning (ML) models remain opaque. Explainability has been advanced as a partial remedy to clarify why AI generates predictions, particularly in high-stakes contexts. Despite ongoing efforts, debates on what constitutes an adequate medical explanation remain unsettled. Yet, explanation has long been a central topic of inquiry in the philosophy of science and

The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to address issues related to their security and privacy. These devices are limited in resources, which complicates their security and the protection of the data that they manage. The paper aims to examine in

Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce planning depends on reliable individual-level absence prediction. Existing regression and classification approaches share a structural limitation; they map features observed at time t to labels at the same time t, reproducing already-realised outcomes rather than predicting futu

Artificial intelligence is transforming our capability to solve biological challenges. In dimensionality bottleneck regimes exacerbated by high-dimensional biological data, neural networks force distinct concepts into the lower dimensions known as superposition. Although this superposition is widely known to hinder interpretability, its impact on corrupting the geometry of latent spaces remains critically overlooked. Here, we utilized sparse autoencoders (SAEs) trained on over 100,000 multiplexe

BY THE PRESIDENT OF THE UNITED STATES OF AMERICA A PROCLAMATION 1. Fertilizers are an essential component of agriculture and food production. Producers of corn, soybeans, wheat, and a variety of other crops need phosphate fertilizers to ensure strong crop yields to feed the population. Food production is critical to human health, farm security, and to […] The post Declaration of Emergency and Authorization for Temporary Duty Free Importation of Phosphate Fertilizer Morocco appeared first on The

Micro-ultrasound ($μ$US) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dependent on clinical experience, leading to substantial inter-observer variability. Machine-learning assistance can reduce this variability; however, training reliable deep models is challenging because supervision is sparse and noisy -- typically limited to core-level histopathology outcomes (e.g., cancer grade and its

Artificial intelligence (AI) systems are increasingly integrated into daily life, with millions now using AI chatbots built on Large Language Models (LLMs) for companionship. Both humanlike AI qualities and user predispositions to anthropomorphize relate to social consequences, such as increased trust, social health benefits, and psychological harms. Populations such as children, older adults, or those with mental health vulnerabilities may be particularly susceptible to anthropomorphism and its

Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric. We introduce a framework that formulates therapeutic response generation as a decision-refinement problem driven by multi-dimensional, human-aligned evaluation. In Stage I, we introduce TheraJudge, an open-source therapeutic evaluator trained via preference-based optimization on human-annotated data to produce

Today's links Gemini is better than search because Google enshittified search: We're All Trying To Find The Guy Who Did This. Hey look at this: Delights to delectate. Object permanence: Microsoft antitrust overturned; Scammer carves C64; RIP Jim Baen; GOP rep to constituent's child: "drop dead" (literally); CCTVs jacked for botnet; Olympic profitability lie; Human factors in health infosec; Exfiltration via computer fans; Congress's summer schedule: 9 working days; Antitrust is political antigra

Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions. We treat them as persistent-state systems: the operative system includes retrievable memories, but also task ledgers, permissions, credentials, commitments, provenance and audit records, shared state, trigger conditions, and externally committed effects linked to those records. The survey reads the literature through six diagnostic axes for each state item, authority, scope, mutab

Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness. We introduce the NHANES Accelerometry Cardiometabolic Benchmark, derived from NHANES 2003-2006, comprising 1,381 adults with hip-worn accelerometry, fasting laboratory biomarkers, dietary intake, and anthropometrics. We evaluate three tabular learning methods -- ridge regression, XGBoost, and the foundati

Portugal's National Health Service signed a deal with digital health company Sword for its AI-assisted virtual physical therapy care.

Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increasing the risk of missed high-risk phenotypes. We propose CW-B, a clinical risk-aligned class-weighted XGBoost pipeline for five-class cardiac discharge phenotyping under real-world class imbalance and missingness. CW-B combines fold-specific class-balanced instance weighting, missingness-indicator augmentation, and classwise error auditing to impro

Edison Scientific and investment firm Population Health Partners are teaming up to leverage AI agents in drug discovery and development.

Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unable to audit. We present SEVA, a structured verification agent that emits evidence alignments, step-by-step reasoning chains, calibrated confidence, and a six-category error diagnosis with actionable fixes. Training such an agent with RL is non-trivial: standard

Lavazza and Vilaça (2024) argue that humanity may face extinction and propose that an “ultimate algorithm” could extract and preserve human values in AI successors. I accept the diagnosis but reject the prescription. This reply introduces the concept of a limit situation —a condition in which AI must act on its own agency because no human remains available to consult—and argues that under such conditions, no value-selection procedure can structurally prevent catastrophe. The obstacle is not the

Human companionship is an essential capability for mobile robots operating in dynamic, human-centered environments. It enables robots to perform tasks such as guidance, assistance, surveillance, and service delivery across various domains, including healthcare, logistics, and public safety. The recent advances in artificial intelligence (AI), particularly in computer vision, deep learning, and sensor fusion, have significantly improved the reliability, adaptability, and contextual understanding