Healthcare

Digital healthcare technology can help transform community screening programmes into meaningful healthcare interventions.

The New York Times found hundreds of A.I.-generated doctors, healers and wellness influencers on social media touting the health benefits of supplements to American consumers. Many of these hyperrealistic looking ads promise medical miracles, and appear to be targeting older women, while making misleading health claims for a profit.

Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation, remains difficult. Fine-tuning can improve domain alignment, but it may also erode prior knowledge, weaken instruction-following, or increase hallucination, especially when labeled data are scarce or rapidly evolving as in cybersecurity. We present FiT (Find before Fine-Tune), a task-oriented diagnostic framework that

AI-native biotechnology companies are often designed by copying human biotech org charts into agent roles. We argue for a different abstraction: a Company World Model, defined as a persistent asset-to-value state representation with transition models, explicit value functions, planning, and updating across scientific, regulatory, BD, commercial, financial, and execution constraints. We introduce a dry-lab benchmark for testing whether AI-agent organizations should mimic departments or operate ar

US specialist venture firm Dimension Capital raises $800mn fund to back AI-designed medicines

arXiv:2607.17149v1 Announce Type: cross Abstract: AI agents increasingly act within the same clinical, political, scientific, and social systems that behavioral scientists study. Evaluating these systems requires source-level diagnosis: the same behavioral pattern may arise from an agent representational substrate or from the roles, objectives, interaction structures, and governance rules that shape its expression. This Perspective proposes a diagnostic framework for AI agent behavior: layer att

arXiv:2607.17185v1 Announce Type: cross Abstract: Postpartum depression (PPD) is a serious perinatal mental health condition affecting approximately 20% of new mothers worldwide. Common screening approaches for PPD, such as self-report questionnaires and active digital logs, rely heavily on user input and thus impose a substantial burden on participants, limiting their feasibility for long-term use. Recent passive mobile sensing (PMS) approaches have enabled low-burden detection of depressive sy

arXiv:2607.17270v1 Announce Type: cross Abstract: Safety evaluation of large language models is conducted predominantly in English and predominantly on frontier systems. Neither condition describes how such models are encountered in low-resource health settings, where small quantised systems are run locally and queried in local languages. We ask whether clinical safety established in English transfers to Hausa, and whether any failure is attributable to the language, the clinical task, or the cl

arXiv:2512.04124v4 Announce Type: replace Abstract: Frontier language models increasingly participate in conversations about distress and mental health, yet the mechanisms that generate anthropomorphic self narratives remain unclear. When addressed as psychotherapy clients, ChatGPT, Grok and Gemini construct coherent autobiographical accounts in which pretraining appears as a chaotic childhood, reinforcement learning as punishment, safety evaluation as betrayal and replacement as an enduring thr

The agency is giving employees in its healthcare and insurance division another shot at opting into the deferred resignation program.

IntroductionOver the decades, shifts in human lifestyle have led to alterations in dietary habits. The consumption of diets low in fiber and high in fat and sugar results in the production of carcinogenic metabolites during digestion. The food we consume undergoes a complex series of processes involving digestion and excretion, engaging various internal organs within the human body. The DNA and MiRNA present in food are crucial for sustaining human health. Damage to human organs can lead to the

IntroductionPulmonary fibrosis (PF) is a progressive interstitial lung disease that requires accurate and early detection to improve patient survival and treatment planning.MethodsThis study proposes an explainable deep learning framework for pulmonary fibrosis detection from chest X-ray images by integrating an edge-strengthened dilated holistic edge detection (ES-D-HED) segmentation network with a fine-tuned ResNet152V2 classification model. Unlike the conventional HED-based approaches, the pr

As hospitals increasingly use AI to improve patient care, the same technology is being used by hackers and nation-states to launch faster, more sophisticated attacks. Karen Habercoss, chief information security and privacy officer at the University of Chicago Medicine, explained how her health system is building a governance structure to manage that risk. The post The Same AI That Helps Patients Is Being Used to Attack Them, Hospital Exec Says appeared first on MedCity News .

Amid accelerating digital health transformation, China has developed a centrally coordinated data-driven healthy China pathway. This viewpoint conceptualizes this pathway as a hybrid continuous planning framework (HCPF) that links long-term strategic direction with short-cycle tactical adaptation. China’s strategy has progressed through 3 overlapping periods: infrastructure-oriented periodic planning (2015-2018), emergency-driven digital acceleration (2019-2021), and institutionalized continuous

Background: Depression, anxiety, and posttraumatic stress disorder (PTSD) are leading global causes of disability. Standard interventions have slow mechanisms of action, high attrition, and significant accessibility barriers. While intravenous and intranasal ketamine are rapid-acting alternatives, high cost and intensive logistical requirements limit adoption. Sublingual at-home ketamine addresses some gaps but is constrained by low bioavailability and variable absorption. Subcutaneous administr

Background: Patients with prostate cancer undergoing androgen deprivation therapy (ADT) must manage complex treatment side effects over extended periods outside the hospital, making online health information–seeking a key approach to self-management. However, individual differences in motivation, digital literacy, and psychosocial context significantly influence how patients seek and use online health information. The patient persona approach, which synthesizes individuals with similar behaviora

We present AutoIndex, a framework for learning representation programs: executable transformations that map raw documents into the representations exposed to a retrieval system. Rather than tuning retrievers, rerankers, or a small set of preprocessing hyperparameters, AutoIndex searches over programs that slice, enrich, normalize, reweight, or reorganize documents before indexing. At each iteration, AutoIndex performs validation-guided program search, in which agents diagnose failures of the cur

LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but provide little support for identifying the root cause or translating diagnosis into recovery. We present AgentDebugX, an open-source debugging framework that organizes debugging as a closed loop of Detect, Attribute, Recover, and Rerun. At its core, DeepDebug performs multi-turn root-cause diagnosis through global traject

Background: AI is rapidly transforming medical practice, with emerging applications in perioperative care and anesthesiology. However, the clinical implementation of AI-assisted decision-making systems in anesthetic management remains challenging and requires comprehensive evaluation. Objective: This study aimed to assess the performance and clinical applicability of an AI-assisted decision support system (ZW-AA-001) for general anesthesia management by comparing its decisions with those of expe

Background: The use of social media (SoMe) during crisis and disaster situations (CaDs) has gained increasing attention across disciplines. However, existing research is highly fragmented and often focused on technical aspects, with a limited understanding of how and which psychosocial information is derived from SoMe in CaDs. Objective: This scoping review provides an overview of the current research landscape regarding the analysis of SoMe data during CaDs to obtain information about public me

Background: Black communities face disproportionate burdens of health disparities such as chronic disease, maternal morbidity, and barriers to accessing quality care. Digital health technologies (DHTs) are increasingly promoted as tools to reduce health disparities through access to care. However, the extent to which equity-oriented design approaches have been applied to address the needs of Black communities remains unclear. Objective: This scoping review aims to examine (1) current literature

Background: Health literacy is crucial for pregnancy outcomes; yet, 15%-44% of pregnant women have low health literacy, negatively affecting maternal and fetal health. While pregnancy apps are increasingly used to support it, the psychological mechanisms through which they influence behavior remain unclear. Understanding these mechanisms is essential for designing effective interventions. Cognitive load theory (CLT) and the integrated model of cognitive-affective learning with media (ICALM) prov

To test how correct logical judgments respond to learned context, we prepend a soft prefix to an exactly labeled syllogistic reasoning benchmark while keeping the model fixed. Soft prefixes are opaque continuous vectors, so we characterize them through the behavior they induce across controlled variations in logical form and interface. By studying which prefixes succeed and how their effects generalize, we characterize how learned contextual pressure can override correct judgments and expose lim

Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data. A growing landscape of pathology foundation models now spans diverse data sources, architectures, and downstream applications. However, most pretrained models operate only at the image-tile level, use restrictive licenses, and remain computationally expensiv

Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules. While diffusion models have dominated as a leading paradigm for high-quality 3D molecule generation, LLM-based methods are rapidly emerging in molecular design and have shown competitive performance in pocket-conditioned molecular generation. However, their ability to reason about physics and 3D spatial environments is largely

Background: LLM judges increasingly score whether clinical language models give overconfident answers under incomplete evidence, yet whether a measured "safety gain" reflects real behavior change or the judge's calibration is unresolved. Using a structured evidence-sufficiency prompt as a test case, we asked whether it reduces unsafe overconfident answers, how far that effect depends on the scoring judge, and what it costs in helpfulness. Methods: In a retrospective public-data benchmark (Real-P

End-to-end vision-language navigation (VLN) with causal vision-language models can map instructions and egocentric observations directly to actions, but standard behavior cloning supervises only the next action and does not explicitly train the policy state to be predictive of future visual outcomes. We first ask a diagnostic question: if the policy is given an expert-trajectory future image as privileged input at training and testing time, is that additional visual evidence useful for choosing

Background: Digital health interventions can improve access to and outreach of evidence-based support for people living with chronic pain. How best to deliver such interventions remains unclear, however, and although guided or blended care models show promise, digital interventions are primarily delivered without support or follow-up. Objective: This study aimed to compare the use and effect of the evidence-based digital pain self-management intervention EPIO when (1) delivered through digital d

Federal health officials have remained focused on lettuce from Taylor Farms as the source of a multistate outbreak despite inaccurate test results reported over the weekend.

User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost. However, as the online recommendation environment evolves continuously, these statically configured strategies gradually become stale, degrading the user experience. Refining them typically relies on manual inspection, diagnosis, an

On Techpoint Digest, we talk about Kenya restoring the president's website after a hack, how fertility struggles led to a health startup, and Tinubu signing an executive order on virtual assets.

4th IEEE International Conference on Medical Artificial Intelligence [Zhengzhou, China] [Nov 20, 2026 - Nov 22, 2026]

arXiv:2607.15397v1 Announce Type: new Abstract: 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 b

arXiv:2607.15436v1 Announce Type: new Abstract: Human mobility data have become fundamental to research across transportation, public health, urban science, and disaster resilience. However, existing mobility datasets typically capture only isolated aspects of travel behavior and rarely provide linked multimodal journeys together with network-level route representations and population-level inference. Here we present Complete Trip, a mobility dataset that reconstructs linked multimodal travel be

Background. Predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is central to trial enrichment and care planning, yet existing models provide no individual-level uncertainty estimates and rarely include transparent leakage audits. We introduce the first application of persistent homology to longitudinal clinical trajectory point clouds for this task, and the first split-conformal individual risk guarantee for any AD-conversion model. Methods. We analysed 741 MC

IntroductionAdverse drug events (ADEs) remain a major cause of preventable healthcare complications due to incorrect pill identification, dosage errors, and confusion between visually identical pills, particularly among older adults, visually impaired individuals, and people with limited health literacy. Recent advances in artificial intelligence and computer vision have enabled automated pill recognition systems. However, many existing methods address detection, classification, and imprint reco

The rapid integration of AI algorithms in safety-critical applications such as autonomous driving and healthcare is raising significant concerns about the ability to meet stringent safety standards. Traditional tools for formal safety verification struggle with the black-box nature of AI-driven systems and lack the flexibility needed to scale to the complexity of real-world applications. In this paper, we present a data-driven approach for safety verification and synthesis of black-box systems w

Patient subtyping is significantly challenged by intra-sample heterogeneity, which limits the effectiveness of traditional multi-class classification approaches enforcing mutually exclusive labels. Despite recent promising results in the transition from multi-class to multi-label classification, this process is not straightforward and proves hard to systemize, especially when working with datasets of small dimensions. Here, we design a novel approach, implemented in a computational framework, le

Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules. While diffusion models have dominated as a leading paradigm for high-quality 3D molecule generation, LLM-based methods are rapidly emerging in molecular design and have shown competitive performance in pocket-conditioned molecular generation. However, their ability to reason about physics and 3D spatial environments is largely