Healthcare

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Marina Raguž, Domagoj Dlaka, Marko Švaco, Petar Marčinković, Dominik Romić, Filip Šuligoj, Bojan Šekoranja, Darko Chudy, Bojan Jerbić

arXiv:2607.26317v1 Announce Type: new Abstract: Psychometric calibration for educational tests typically requires costly human response data. Large language models (LLMs) simulated examinees offer a promising route to early calibration, but their responses are too accurate and too uniform. We propose Cognitive Diagnostic Profiling (CDP), a zero-shot framework that prompts LLMs to simulate plausible examinees with diverse cognitive profiles: binary attribute-mastery patterns are rendered as natur

arXiv:2607.26886v1 Announce Type: cross Abstract: When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show that this confabulation is not random. It is structured by who the patient is said to be. Across chest X-ray, brain MRI, and dermatology, Claude Opus-4.7, GPT-5.4, and Gemini-3.1-Pro are each queried with only a demographic descriptor and no image, and changing the descriptor systematically shifts t

arXiv:2503.10647v2 Announce Type: replace-cross Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.0 Flash and OpenAI ChatGPT-4o, across three dimensions: consistency under rephrased inputs, susceptibility to irrelevant prompt content, and responsiveness to added clinical context. We designed 52 clinical scenarios and modified each under controlled conditions. For consistency, scenarios were rephrased with demographic, wording, and exam

arXiv:2507.04398v3 Announce Type: replace-cross Abstract: Generative AI is becoming part of academic writing, but its educational value depends on how control is shared between learner and system. This study examined an agency gap: performance differences that may arise when AI agent initiative is misaligned with learners' generative AI literacy. Seventy-nine medical and nursing students completed two multimodal analytical writing tasks using healthcare simulation data visualisations. They were

OpenAI is trying to reassure employees that the business is healthy as competition emerges from Anthropic as well as a host of open-source players.

Weng previously served as the VP of AI Safety Research at OpenAI.

Background: As digital technologies become increasingly embedded in daily life, their roles in mental health care have expanded and diversified. Digital tools are being explored as interventions for obsessive-compulsive disorder (OCD) across the care continuum, including symptom recognition, access to care, treatment, and self-management. However, there is limited empirical understanding of how individuals living with OCD use digital technologies in situ to navigate their health care journeys or

Is more data about your body actually good for you?

When 54 international experts assessed AI-generated disinformation threats, they revealed a surprising pattern: while video deepfakes received the highest average threat ratings in the political domain (M = 6.31/7), the pattern differed in the health domain, where AI-generated text received the highest average rating (M = 5.80). The post Experts disagree on how to fight AI disinformation, but agree that health and politics need different solutions first appeared on HKS Misinformation Review .

Background: Hypospadias is a common congenital malformation requiring surgery. Caregivers face substantial perioperative information needs, and large language models (LLMs) offer a potential health education channel, but their performance in pediatric urology and the relation between citation accuracy and clinical content safety lack systematic evaluation. Objective: This study aimed to evaluate 5 LLMs (ChatGPT-4o, Gemini-2.5-Pro, OpenEvidence, Zhipu Qingyan, and DeepSeek) for pediatric hypospad

Background: Identifying traits of narcissistic personality disorder (NPD) is clinically challenging, yet early detection can significantly improve outcomes. Online forums have become a major source of self-expression, offering new opportunities to understand mental health. However, analyzing this complex language requires new tools. Objective: This study aims to determine whether a machine learning model could be trained to reliably detect language patterns associated with NPD traits in Reddit p

High-stakes decision systems in credit scoring, fraud detection, healthcare, and industrial safety require reliable uncertainty quantification under severe class imbalance and asymmetric error costs. Standard marginal conformal prediction (CP) provides valid overall coverage guarantees; however, we show that it severely under-covers rare, costly minority classes, with minority-class coverage dropping to as low as 0.5% on certain datasets. To characterize and address this limitation, we conduct a

Watches, bands, and rings—if you want to digitally monitor your fitness, more companies than ever are selling devices to do it. And more Americans than ever now own at least one wearable health device. But what are the companies that make fitness trackers doing to protect our sensitive data from prying eyes? A lot less than they could be, it turns out. We're explaining what companies can do to protect your health data, and more, with our EFFector newsletter . JOIN OUR NEWSLETTER For over 35 year

With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-grained free-form descriptions. Existing evaluations often focus on generation quality or task performance, making it difficult to diagnose information coverage and description reliability. We propose MMAC, a \textbf{M}assive \textbf{M}ulti-dimensional benchmark for \textbf{A}udio \textbf{C}aptioning. MMAC contains 5,638 audio clips from more than 20

PatientAgentBench generates a synthetic patient health record, a realistic clinical vignette, and a patient agent that converses with the AI system under evaluation, to capture what a patient-facing agent actually has to do.

The visionary PhysioNet platform launched 25 years ago, based on a system developed at MIT in the 1970s. It has become one of the most comprehensive biomedical and clinical data repositories in existence.

In this edition of AI Prognosis: A conversation about benchmarking leading clinical chatbots, investor view on AI in biopharma, and more.

When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show that this confabulation is not random. It is structured by who the patient is said to be. Across chest X-ray, brain MRI, and dermatology, Claude Opus-4.7, GPT-5.4, and Gemini-3.1-Pro are each queried with only a demographic descriptor and no image, and changing the descriptor systematically shifts the diagnosis returned. Claude concentrates sharply

A standardized framework for regulating the safety and efficacy of AI in healthcare has yet to be established.

Complaint alleges telehealth provider shared consumers’ sensitive health information with third-party advertising platforms despite promising patient privacy The Federal Trade Commission, joined by Utah and California, by and through Los Angeles County Counsel, today sued Hims & Hers alleging that the telehealth provider shared consumers’ sensitive health information about medical conditions with third-party advertising platforms despite claiming its services maintain consumers’ privacy and dece

Democrat Andy Beshear sent a letter to the Republican Senator who has been absent from his public duties and faces questions about his health status.

Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear whether they truly rely on these visual states. Existing benchmarks are limited both by task collections with narrow coverage or partially text-solvable samples and by evaluations that emphasize final answers without diagnosing how intermediate visual states are generated, rendered, and used. We introduce See2Think, a unified evaluation framework compri

Background: Digital multidomain interventions hold promise for dementia risk reduction; however, populations at higher dementia risk, including those experiencing socioeconomic and educational disadvantage, remain underrepresented in trials, and engagement with digital interventions often declines over time. Coproduction and blended models that combine digital tools with human support may improve reach, acceptability, usability, and sustained engagement. Designing interventions that are usable a

Artificial intelligence is rapidly becoming part of everyday infrastructure–in some places. It helps write emails and software code, filters job applications, powers recommendation systems, and is increasingly being integrated into education, health care, finance, and public administration. Industry leaders talk about “AI for everyone,” while governments rush to publish national AI strategies and build sovereign compute. Yet over the past decade, working on digital inclusion and digital literacy

Policymakers, employers, and professional organizations have opportunities to design work environments that make us healthier, write Lilly Springer, David Slusky, and Anupam B. Jena.

The online data repository Kaggle has removed a problematic dataset for violating intellectual property rights months after it was flagged by sleuths for containing celebrity photos and lacking information on data provenance or confirmed medical diagnoses. Researchers had used the images to train machine learning algorithms to detect stroke, but the dataset instead contained images … Continue reading Kaggle removes problematic stroke dataset for copyright infringement

Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Yet, existing deep learning approaches require dataset-specific training, large labeled corpora, and repeated adaptation to new sensor settings or activity taxonomies. Retrieval-Augmented Generation for Human Activity Recognition (RAG-HAR) addresses this by framing HAR as a training-free, retrieval-augmented task, in which statistical descriptions

As LLMs compete for doctors' attention, some developers say the science of benchmarking their AI tools for safety and accuracy is flawed.

Govender succeeds Leslie Moodley, who announced his early retirement earlier this month.

Oregon Institution Lacks Funds to Move Away From Animal Testing Ryan Quinn Wed, 07/29/2026 - 03:00 AM The Trump administration is pushing to end animal testing, but Oregon Health and Science University said federal dollars weren’t available to turn its National Primate Research Center into a sanctuary. Byline(s) Ryan Quinn

What We Know About the Government’s Investigations Into Medical Schools Johanna Alonso Wed, 07/29/2026 - 03:00 AM Initial findings from probes into medical schools’ admissions practices have left experts questioning the government’s evidence and analysis. Byline(s) Johanna Alonso

arXiv:2607.25301v1 Announce Type: new Abstract: Resting heart rate is an established marker of cardiovascular risk, but population-scale measurement has depended on clinical or survey instruments. We ask whether passively sensed consumer-wearable physiology recovers the socioeconomic gradient established in clinical cohorts. Using 19.1 million quality-filtered photoplethysmography readings from 18,734 opt-in users of the Welltory app, we computed cohort-adjusted mean daytime resting heart rate p

arXiv:2607.24768v1 Announce Type: cross Abstract: Prenatal care is an important preventive service designed to improve outcomes for pregnant individuals. The American College of Obstetricians and Gynecologists (ACOG) recently introduced guidelines advocating tailored prenatal care, called PATH (Plan for Tailored Healthcare). We present PATHFinder Agent(Planner for Appropriate Tailored Healthcare), an end-to-end conversational agentic system that gathers patient health and social context through

arXiv:2607.24775v1 Announce Type: cross Abstract: Artificial intelligence (AI) chatbots are increasingly deployed in domains where empathy is essential, including healthcare, education, and customer service. However, their capacity to sustain authentic human moments remains structurally limited. This paper introduces two interlinked conceptual models to explain and address this limitation. First, the Human-Moment Gap Framework (HMGF) identifies three structural empathy deficits in AI-mediated in

arXiv:2311.18424v3 Announce Type: replace-cross Abstract: Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together. By understanding AI as 'sociotechnical' where the social and the technical nature of the work and the models are inseparable, we explore the AI development workflow and how stakeholders navigate the challenges and tensions of sharing and generating knowledge across dis

Popular telehealth provider Hims & Hers "shared consumers’ sensitive health information with third-party advertising platforms such as Meta and Snap despite promising to protect patient privacy," the federal government alleges.

The Food and Drug Administration's (FDA) plans to centralize and enhance key functions across the agency. These changes will reduce redundancies, improve efficiency, and advance alignment to better serve the American public.

Hollanek and Nowaczyk-Basińska (2024) analyse the harms of AI-enabled re-creation services and offer four recommendations to providers. I accept their diagnosis but argue that their prescription shares a premise with the industry it seeks to reform: that a responsible provider remains present to carry it out. All four recommendations (retirement procedures, meaningful transparency, adult-only access, and mutual consent) are addressed to a continuing operator. Yet the paper’s own opening example,

Adverse drug reactions (ADRs) are caused by medication and are considered a serious issue in healthcare when there is simultaneous use of different medications resulting in drug-drug interaction (DDI). Traditional approaches mostly focus on the effects caused by a single drug, and they fail to capture the side effects from drug combinations. In this research work, a deep learning-based DrugPred framework is proposed to predict ADR risks by integrating individual drug effects, interaction statist