Normal view
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cs.AI, q-bio.NC updates on arXiv.org
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A Human-Centered Privacy Approach (HCP) to AI
arXiv:2602.04616v1 Announce Type: cross Abstract: As the paradigm of Human-Centered AI (HCAI) gains prominence, its benefits to society are accompanied by significant ethical concerns, one of which is the protection of individual privacy. This chapter provides a comprehensive overview of privacy within HCAI, proposing a human-centered privacy (HCP) framework, providing integrated solution from technology, ethics, and human factors perspectives. The chapter begins by mapping privacy risks across
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cs.AI, q-bio.NC updates on arXiv.org
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Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
arXiv:2602.04678v1 Announce Type: cross Abstract: Time series forecasting in real-world applications requires both high predictive accuracy and interpretable uncertainty quantification. Traditional point prediction methods often fail to capture the inherent uncertainty in time series data, while existing probabilistic approaches struggle to balance computational efficiency with interpretability. We propose a novel Multi-Expert Learning Distributional Labels (LDL) framework that addresses these
Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
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cs.AI, q-bio.NC updates on arXiv.org
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DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data
arXiv:2503.01733v3 Announce Type: replace-cross Abstract: Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels. However, the fixed granularity of these labels often constrains their practical utility, failing to capture the subtle, household-specific n
DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data
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Nature - Issue - nature.com science feeds
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Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes
Nature, Published online: 04 February 2026; doi:10.1038/s41586-025-10087-xA multiancestry phenome-wide analysis of copy number variants in the UK Biobank genomes increases power to detect genetic associations with complex traits across human populations.
Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes
Nature, Published online: 04 February 2026; doi:10.1038/s41586-025-10087-x
A multiancestry phenome-wide analysis of copy number variants in the UK Biobank genomes increases power to detect genetic associations with complex traits across human populations.-
Nature - Issue - nature.com science feeds
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‘It means I can sleep at night’: how sensors are helping to solve scientists’ problems
Nature, Published online: 04 February 2026; doi:10.1038/d41586-026-00212-9Continual automated data collection was once possible only for the world’s richest labs. Now, sensor systems are commonplace and can be accessed from an app.
‘It means I can sleep at night’: how sensors are helping to solve scientists’ problems
Nature, Published online: 04 February 2026; doi:10.1038/d41586-026-00212-9
Continual automated data collection was once possible only for the world’s richest labs. Now, sensor systems are commonplace and can be accessed from an app.-
cs.AI, q-bio.NC updates on arXiv.org
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Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
arXiv:2602.02629v1 Announce Type: cross Abstract: The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training Artificial Intelligence (AI) models. However, stringent privacy regulations such as GDPR and HIPAA have created data silos that prevent centralized training. Federated Learning (FL) has emerged as a promising solution that enables collaborative model training without sharing raw patient data. Despite
Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
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STAT

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STAT+: AI doctors are coming. Should FDA make sure they’re safe?
When is an AI doctor a medical device? Call it a sign of things to come. A startup called Doctronic made a splash recently when it announced the use AI to renew prescriptions without clinician input in the state of Utah. Something didn’t sit right with me about the announcement. Sure it got approval from Utah, but why isn’t it a medical device subject to Food and Drug Administration review? The company claimed it was “the practice of medicine” and so exempt from FDA authority. That didn’t see
STAT+: AI doctors are coming. Should FDA make sure they’re safe?
When is an AI doctor a medical device?
Call it a sign of things to come. A startup called Doctronic made a splash recently when it announced the use AI to renew prescriptions without clinician input in the state of Utah. Something didn’t sit right with me about the announcement. Sure it got approval from Utah, but why isn’t it a medical device subject to Food and Drug Administration review? The company claimed it was “the practice of medicine” and so exempt from FDA authority. That didn’t seem entirely right either.
So I did some asking around and after talking to over a dozen executives, legal scholars, and policy experts, it turns out the question is not nearly as clear-cut as Doctronic would have us believe. Indeed, it appears the company may be planning to market a medical device without authorization. In my story, I explain the law and why it all matters.
Continue to STAT+ to read the full story…


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STAT

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STAT+: AI could soon renew prescriptions without clinician help. Should the FDA make sure it’s safe?
Utah’s recent announcement that it was partnering with a health tech startup that will use artificial intelligence to renew drug prescriptions may offer a glimpse of the futuristic version of AI medicine that’s long been foretold by technologists and venture capitalists. But it’s only possible because of some pre-approved rule breaking — and may prove a broader test of the Food and Drug Administration’s authority to evaluate a new wave of clinical AI products, according to interviews with exe
STAT+: AI could soon renew prescriptions without clinician help. Should the FDA make sure it’s safe?
Utah’s recent announcement that it was partnering with a health tech startup that will use artificial intelligence to renew drug prescriptions may offer a glimpse of the futuristic version of AI medicine that’s long been foretold by technologists and venture capitalists.
But it’s only possible because of some pre-approved rule breaking — and may prove a broader test of the Food and Drug Administration’s authority to evaluate a new wave of clinical AI products, according to interviews with executives and experts.
In January, Utah regulators said they had signed an agreement with a startup called Doctronic to launch an AI system that will perform a clinical evaluation of patients and, when deemed appropriate, renew some 200 common medications autonomously.
Continue to STAT+ to read the full story…


© Illustration: Camille MacMillin/STAT; Photos: Adobe
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npj Digital Medicine
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Digital intervention <i>mylovia</i> improves sexual functioning in women with sexual dysfunction in randomized controlled trial
npj Digital Medicine, Published online: 03 February 2026; doi:10.1038/s41746-026-02385-zDigital intervention mylovia improves sexual functioning in women with sexual dysfunction in randomized controlled trial
Digital intervention <i>mylovia</i> improves sexual functioning in women with sexual dysfunction in randomized controlled trial
npj Digital Medicine, Published online: 03 February 2026; doi:10.1038/s41746-026-02385-z
Digital intervention mylovia improves sexual functioning in women with sexual dysfunction in randomized controlled trial-
Omics in Gastric
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Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer
Gut. 2026 Jan 30:gutjnl-2025-337247. doi: 10.1136/gutjnl-2025-337247. Online ahead of print.ABSTRACTBACKGROUND: Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear.OBJECTIVE: To delineate multifactorial aetiologies and clinically actionable subtypes/b
Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer
Gut. 2026 Jan 30:gutjnl-2025-337247. doi: 10.1136/gutjnl-2025-337247. Online ahead of print.
ABSTRACT
BACKGROUND: Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear.
OBJECTIVE: To delineate multifactorial aetiologies and clinically actionable subtypes/biomarkers of gastric cancer through integrative proteogenomic, microbial and environmental exposure profiling.
DESIGN: We established a multiomics atlas of paired tumour, adjacent mucosa tissues and blood from 154 treatment-naïve Taiwanese patients, integrating whole-exome sequencing, RNA-seq, proteome and phosphoproteome profiling with carcinogen signatures, HP status, microbiome composition and refined anatomical mapping. Cell-based functional assays tested carcinogen effects. Microbial subtype was assessed in an independent cohort.
RESULTS: A polycyclic-aromatic-hydrocarbon signature, dibenz[a,h]acridine, emerged as a high-risk exposure promoting invasion, immune suppression and poor survival, significantly exceeding nitrosamine-linked risk in this cohort. Multilayer integration defined three initiation ecologies: HP-driven inflammatory, non-HP microbiome-enriched immune-silent and HP-free microbially depleted states. Among HP-negative tumours, a Streptococcus-enriched subtype associated with tight-junction (CLDN18.2/ZO-1/OCLN) disruption and epithelial-mesenchymal transition, whereas a subset of clinically aggressive cases retained CLDN18.2-high epithelial-stable subtype for therapeutic accessibility. An independent cohort revealed gastric juice-derived Streptococcus anginosus abundance inversely correlated with tight-junction proteins. Anatomical mapping reveals location-specific, sex-specific, subtype-specific oncogenic networks and kinase activity, including CDK4 activation in clinical biomarker-negative tumours. Decision-tree models combining exposure and proteome-immune states refined recurrence and survival prediction beyond stage.
CONCLUSION: This proteogenomic framework defines exposure-informed and microbiome-informed gastric cancer subtypes, providing a molecular schema for patient stratification, prevention and actionable therapeutic vulnerabilities.
PMID:41617485 | DOI:10.1136/gutjnl-2025-337247
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Pulmonary nodule
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Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach
Discov Oncol. 2026 Jan 29;17(1):178. doi: 10.1007/s12672-025-03646-1.ABSTRACTPulmonary nodules are a common radiological finding that can be classified as either benign or Malignant, with significant clinical implications. The early detection of malignant nodules is critically important for improving the prognosis of lung cancer, which remains the leading cause of cancer-related mortality worldwide. Traditional imaging techniques have Limitations in accurately classifying pulmonary nodules. Liqu
Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach
Discov Oncol. 2026 Jan 29;17(1):178. doi: 10.1007/s12672-025-03646-1.
ABSTRACT
Pulmonary nodules are a common radiological finding that can be classified as either benign or Malignant, with significant clinical implications. The early detection of malignant nodules is critically important for improving the prognosis of lung cancer, which remains the leading cause of cancer-related mortality worldwide. Traditional imaging techniques have Limitations in accurately classifying pulmonary nodules. Liquid biopsy, a minimally invasive method that evaluates circulating components in the Blood, presents promising diagnostic potential in this context. This study aims to evaluate the diagnostic capacity of multiple liquid biopsy biomarkers for early and accurate differentiation between benign and Malignant pulmonary nodules. Accordingly, we conducted a comprehensive study involving a meta-analysis, selecting 16 eligible studies that utilised liquid biopsy to assess various circulating biomarkers in the diagnostic yield. The most significant results were linked to circulating free DNA (cfDNA). However, other components, including circulating tumour cells (CTCs), microRNAs/pfeRNAs, extracellular vesicles (EVs), serological markers, and imaging techniques, also provided valuable information. Similarly, integrating multi-omics data with machine learning models has been shown to enhance the ability to differentiate between benign and malignant pulmonary nodules, thereby supporting early diagnosis and improved management for patients with lung cancer.
PMID:41612093 | PMC:PMC12855667 | DOI:10.1007/s12672-025-03646-1
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Journal of Medical Internet Research
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The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study
Background: Online health communities have evolved into digital marketplaces where physicians have to compete for patients. Existing research examines physician-patient dynamics through a patient-centric lens, treating physicians as passive recipients of ratings and reviews, while the strategic role of physician self-disclosure remains unexamined. This gap constrains a comprehensive understanding of how physicians can actively shape patient decisions, making the investigation of strategic self-d
The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study
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STAT

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Opinion: Why I decided to share all my health information with ChatGPT Health
When I first heard about OpenAI’s ChatGPT Health, I felt a familiar itch. Since being diagnosed with a malignant brain tumor 18 years ago, at age 29, I’ve developed a deep curiosity about my own health. That curiosity has driven me to enroll in numerous studies, connect my health records to the NIH All of Us research program, and even donate my brain tissue for research-grade genomic sequencing.Read the rest…
Opinion: Why I decided to share all my health information with ChatGPT Health
When I first heard about OpenAI’s ChatGPT Health, I felt a familiar itch.
Since being diagnosed with a malignant brain tumor 18 years ago, at age 29, I’ve developed a deep curiosity about my own health. That curiosity has driven me to enroll in numerous studies, connect my health records to the NIH All of Us research program, and even donate my brain tissue for research-grade genomic sequencing.


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MRD
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Liquid biopsy in cancer diagnosis and prognosis: a paradigm shift in precision oncology
Front Mol Biosci. 2026 Jan 12;12:1708518. doi: 10.3389/fmolb.2025.1708518. eCollection 2025.ABSTRACTLiquid biopsy has emerged as a transformative tool in precision oncology, offering a minimally invasive approach for cancer detection, monitoring, and treatment guidance. Unlike traditional tissue biopsies, which are invasive and limited by tumor accessibility and sampling bias, liquid biopsy enables real-time tumor assessment through the analysis of circulating biomarkers in blood and other biofl
Liquid biopsy in cancer diagnosis and prognosis: a paradigm shift in precision oncology
Front Mol Biosci. 2026 Jan 12;12:1708518. doi: 10.3389/fmolb.2025.1708518. eCollection 2025.
ABSTRACT
Liquid biopsy has emerged as a transformative tool in precision oncology, offering a minimally invasive approach for cancer detection, monitoring, and treatment guidance. Unlike traditional tissue biopsies, which are invasive and limited by tumor accessibility and sampling bias, liquid biopsy enables real-time tumor assessment through the analysis of circulating biomarkers in blood and other biofluids. This review provides a comprehensive overview of recent advances in liquid biopsy, with a focus on circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), non-coding RNAs, extracellular vesicles (exosomes), and secreted proteins. These biomarkers offer valuable insights into tumor biology, supporting applications in early diagnosis, prognosis, treatment response monitoring, and minimal residual disease detection across various cancer types. We also discuss state-of-the-art methodologies, including next-generation sequencing, digital PCR, microfluidics, proteomics, and emerging artificial intelligence-based approaches that enhance the sensitivity, specificity, and scalability of liquid biopsy assays. Clinical studies demonstrate the potential of liquid biopsy for tailoring targeted therapies, predicting resistance mechanisms, and identifying tumor recurrence earlier than conventional methods. Furthermore, FDA-approved assays and ongoing phase III and IV clinical trials highlight its growing integration into routine clinical practice. Beyond technical innovations, this review examines the global landscape of liquid biopsy, emphasizing opportunities and challenges for implementation across diverse healthcare settings. Disparities in access, particularly between high-income and low- and middle-income countries, underscore the need for strategies that ensure equitable adoption of liquid biopsy technologies worldwide. In summary, liquid biopsy represents a paradigm shift in oncology, bridging innovations in cancer diagnostics with clinical applications. By enabling dynamic, personalized, and less invasive cancer management, it holds great promise for improving patient outcomes and advancing precision medicine.
PMID:41602544 | PMC:PMC12832364 | DOI:10.3389/fmolb.2025.1708518
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cs.AI, q-bio.NC updates on arXiv.org
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Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT
arXiv:2601.19380v1 Announce Type: cross Abstract: Using multiple open-access models trained on public datasets, we developed Tri-Reader, a comprehensive, freely available pipeline that integrates lung segmentation, nodule detection, and malignancy classification into a unified tri-stage workflow. The pipeline is designed to prioritize sensitivity while reducing the candidate burden for annotators. To ensure accuracy and generalizability across diverse practices, we evaluated Tri-Reader on multi
Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT
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cs.AI, q-bio.NC updates on arXiv.org
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Is On-Policy Data always the Best Choice for Direct Preference Optimization-based LM Alignment?
arXiv:2508.10530v2 Announce Type: replace Abstract: The alignment of language models~(LMs) with human preferences is critical for building reliable AI systems. The problem is typically framed as optimizing an LM policy to maximize the expected reward that reflects human preferences. Recently, Direct Preference Optimization~(DPO) was proposed as a LM alignment method that directly optimize the policy from static preference data, and further improved by incorporating on-policy sampling~(i.e., pre
Is On-Policy Data always the Best Choice for Direct Preference Optimization-based LM Alignment?
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cs.AI, q-bio.NC updates on arXiv.org
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Demystifying the Roles of LLM Layers in Retrieval, Knowledge, and Reasoning
arXiv:2510.02091v4 Announce Type: replace Abstract: Recent studies suggest that the deeper layers of Large Language Models (LLMs) contribute little to representation learning and can often be removed without significant performance loss. However, such claims are typically drawn from narrow evaluations and may overlook important aspects of model behavior. In this work, we present a systematic study of depth utilization across diverse dimensions, including evaluation protocols, task categories, a
Demystifying the Roles of LLM Layers in Retrieval, Knowledge, and Reasoning
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cs.AI, q-bio.NC updates on arXiv.org
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Rethinking the AI Scientist: Interactive Multi-Agent Workflows for Scientific Discovery
arXiv:2601.12542v2 Announce Type: replace Abstract: Artificial intelligence systems for scientific discovery have demonstrated remarkable potential, yet existing approaches remain largely proprietary and operate in batch-processing modes requiring hours per research cycle, precluding real-time researcher guidance. This paper introduces Deep Research, a multi-agent system enabling interactive scientific investigation with turnaround times measured in minutes. The architecture comprises specializ
Rethinking the AI Scientist: Interactive Multi-Agent Workflows for Scientific Discovery
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cs.AI, q-bio.NC updates on arXiv.org
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AI-generated data contamination erodes pathological variability and diagnostic reliability
arXiv:2601.12946v3 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathologic
AI-generated data contamination erodes pathological variability and diagnostic reliability
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Journal of Medical Internet Research
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Products, Performance, and Technological Development of Ambulatory Oxygen Therapy Devices: Scoping Review
Background: Ambulatory oxygen therapy is prescribed for patients with chronic lung diseases who experience exertional hypoxemia. However, available devices may not adequately meet user requirements, and their performance characteristics are heterogeneous. Objective: This study aims to identify devices available for delivery of ambulatory oxygen therapy, the technologies that they use to generate oxygen, the performance characteristics of each device, and the development status. Methods: We used