❌

Normal view

Received — 30 January 2026 ⏭ Favourites

The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study

✇Journal of Medical Internet Research
By: Quanchen Liu · Pengqing Yin · Jing Fan
30 January 2026 at 06:00
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-disclosure imperative. Objective: This study aims to investigate the relationship between physician self-disclosure breadth (scope of information) and depth (detailed expertise) and patient decision-making, as well as whether regional digital health care level (DHL) moderates these relationships. Methods: We conducted a cross-sectional analysis of observational data to test these relationships. Data were collected from China’s online health care platform Haodf from September to December 2024. Self-disclosure breadth (including clinical performance, academic experience, and social reputation), self-disclosure depth (including expertise coverage, richness, and granularity), and patient decision-making (total visits) were captured through manual content coding and quantitative measurement. We used structured content analysis to extract the disclosure components, informational scope, and descriptive details of each profile. Then, using validated operational formulas, we calculated the composite indices for disclosure breadth and depth based on the coded dimensions. The study generated 1798 final physician samples with complete data across 14 focal variables. The hypotheses were tested using an ordinary least squares regression model, and 4 robustness checks were conducted, including variable substitution and different resampling techniques. Results: In the primary ordinary least squares regression models, self-disclosure breadth was significantly and positively associated with patient visits (β=0.255, 95% CI 0.054-0.456; P=.01), as was self-disclosure depth (β=0.098, 95% CI 0.030-0.167; P=.005). The breadth×DHL interaction was positive and significant (β=0.261, 95% CI 0.061-0.461; P=.01). Similarly, the depth×DHL interaction was positive and significant (β=0.070, 95% CI 0.002-0.138; P=.045). It should be noted that the association for self-disclosure breadth was stronger than that of self-disclosure depth. DHL strengthened the relationship between the disclosure strategies with patient visits. This contextual amplification indicates that DHL serves as a critical boundary condition, determining the degree to which physician self-disclosure strategies translate into patient acquisition outcomes. Conclusions: This study reconceptualizes physicians as strategic agents shaping patient decision-making through purposeful self-disclosure. Different from existing studies treating physicians as passive recipients of ratings and reviews, our research demonstrates that physicians can strategically shape patient acquisition through self-disclosure breadth and depth. This study brings new insights to digital health markets by demonstrating that self-disclosure operates as a viable patient acquisition mechanism, wherein the DHL acts as a critical boundary condition. The findings have real-world implications: (1) physicians can leverage evidence-based disclosure strategies, (2) platforms should implement context-adaptive features, and (3) policymakers should prioritize digital infrastructure investments to enhance physicians' competitive capabilities and patient decision-making quality.
  • ✇STAT
  • Opinion: Why I decided to share all my health information with ChatGPT Health Liz Salmi
    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

✇STAT
By: Liz Salmi
29 January 2026 at 17:30

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…

© Adobe

Liquid biopsy in cancer diagnosis and prognosis: a paradigm shift in precision oncology

✇MRD
By: Rayane da Silva Abreu · Danielle Dias Pinto Ferreira · Natassia Silva de Araujo · Samuel Horita · Tatiana Martins Tilli · Wim Degrave · Aline Dos Santos Moreira · Mariana Caldas Waghabi
28 January 2026 at 19:00

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

Received — 28 January 2026 ⏭ Favourites

Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fakrul Islam Tushar · Joseph Y. Lo
28 January 2026 at 13:00
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 multiple internal and external datasets as compared with expert annotations and dataset-provided reference standards.

Is On-Policy Data always the Best Choice for Direct Preference Optimization-based LM Alignment?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zetian Sun · Dongfang Li · Xuhui Chen · Baotian Hu · Min Zhang
28 January 2026 at 13:00
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., preference candidates generated during the training loop) for better LM alignment. However, we show on-policy data is not always optimal, with systematic effectiveness difference emerging between static and on-policy preference candidates. For example, on-policy data can result in a $3\times$ effectiveness compared with static data for Llama-3, and a $0.4\times$ effectiveness for Zephyr. To explain the phenomenon, we propose the alignment stage assumption, which divides the alignment process into two distinct stages: the preference injection stage, which benefits from diverse data, and the preference fine-tuning stage, which favors high-quality data. Through theoretical and empirical analysis, we characterize these stages and propose an effective algorithm to identify the boundaries between them. We perform experiments on $5$ models~(Llama, Zephyr, Phi-2, Qwen, Pythia) and $2$ alignment methods~(DPO, SLiC-HF) to show the generalizability of alignment stage assumption and the effectiveness of the boundary measurement algorithm.

Demystifying the Roles of LLM Layers in Retrieval, Knowledge, and Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinyuan Song · Keyu Wang · PengXiang Li · Lu Yin · Shiwei Liu
28 January 2026 at 13:00
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, and model architectures. Our analysis confirms that very deep layers are generally less effective than earlier ones, but their contributions vary substantially with the evaluation setting. Under likelihood-based metrics without generation, pruning most layers preserves performance, with only the initial few being critical. By contrast, generation-based evaluation uncovers indispensable roles for middle and deeper layers in enabling reasoning and maintaining long-range coherence. We further find that knowledge and retrieval are concentrated in shallow components, whereas reasoning accuracy relies heavily on deeper layers -- yet can be reshaped through distillation. These results highlight that depth usage in LLMs is highly heterogeneous and context-dependent, underscoring the need for task-, metric-, and model-aware perspectives in both interpreting and compressing large models.

Rethinking the AI Scientist: Interactive Multi-Agent Workflows for Scientific Discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lukas Weidener · Marko Brki\'c · Mihailo Jovanovi\'c · Ritvik Singh · Chiara Baccin · Emre Ulgac · Alex Dobrin · Aakaash Meduri
28 January 2026 at 13:00
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 specialized agents for planning, data analysis, literature search, and novelty detection, unified through a persistent world state that maintains context across iterative research cycles. Two operational modes support different workflows: semi-autonomous mode with selective human checkpoints, and fully autonomous mode for extended investigations. Evaluation on the BixBench computational biology benchmark demonstrated state-of-the-art performance, achieving 48.8% accuracy on open response and 64.4% on multiple-choice evaluation, exceeding existing baselines by 14 to 26 percentage points. Analysis of architectural constraints, including open access literature limitations and challenges inherent to automated novelty assessment, informs practical deployment considerations for AI-assisted scientific workflows.

AI-generated data contamination erodes pathological variability and diagnostic reliability

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongyu He · Shaowen Xiang · Ye Zhang · Yingtao Zhu · Jin Zhang · Hao Deng · Emily Alsentzer · Qingyu Chen · Kun-Hsing Yu · Andrew Marshall · Tingting Chen · Srinivas Anumasa · Daniel Ebner · Dean Ho · Kee Yuan Ngiam · Ching-Yu Cheng · Dianbo Liu
28 January 2026 at 13:00
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 pathological variability and diagnostic reliability. By analysing more than 800,000 synthetic data points across clinical text generation, vision-language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architecture. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence; models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Products, Performance, and Technological Development of Ambulatory Oxygen Therapy Devices: Scoping Review

✇Journal of Medical Internet Research
By: Shohei Kawachi · Mariana Hoffman · Lorena Romero · Magnus Ekström · Jerry A Krishnan · Anne E Holland
28 January 2026 at 03:00
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 medical and engineering databases to identify peer-reviewed papers (eg, MEDLINE, IEEE). Gray literature was used to identify additional descriptions of ambulatory oxygen devices in military medicine, space exploration, or patents. The last search was conducted in September 2025. Documents that described a device that can deliver oxygen in an ambulatory context (defined as weighing less than 10 kg) and were written in English were included. Search results were screened for inclusion by 2 independent reviewers. Data were synthesized by descriptively mapping the performance of each product, the technology used, and the development status of emerging technologies. Results: From 9702 records identified, a total of 166 met eligibility criteria (106 scientific publications and 60 gray literature). We identified 33 portable oxygen concentrators (POCs; 29 commercially available), 10 oxygen cylinders, and 6 portable liquid oxygen (LOX) devices. The POC products showed a trade-off between portability and oxygen delivery capacity (maximum flow rate ranging from 2.0 to 6.0 L/min; device weight ranging from 1.0 to 9.1 kg). Pressure swing adsorption with zeolite was the most common oxygen generation technology in POCs on the market. The mean maximum continuous operating time of POCs was 3.8 hours. Two prototype POCs (maximum flow rate of 4-6 L/min and device weight of 8-9 kg) were developed for space exploration using modified adsorbents. LOX devices were the lightest and had the longest continuous operating time. Innovations in delivery included the downsizing of a POC by using nanozeolite as an adsorbent and pulse oximeter oxygen saturation (SpO2)–targeted automatic titration of oxygen delivery based on the user’s SpO2. Conclusions: This scoping review is the first study to integrate medical, engineering, and gray literature on ambulatory oxygen devices and their development. Although prior literature has narratively explained the products and technologies, no previous research has systematically investigated them. This review showed that POCs available to consumers may not meet the needs of patients in terms of flow rate, portability, and operating time. LOX devices offered superior performance but are limited by high costs. Limitations of this review include the difficulty of comparing product performance across oxygen delivery settings and that the records were largely obtained from English-language sources. Innovation in ambulatory oxygen technology has been limited over the past decade, highlighting urgent need for research and development of new lightweight devices with higher oxygen delivery. Clinical Trial: OSF Registries 10.17605/OSF.IO/QS7FX; https://osf.io/qs7fx

Exploring the key molecular mechanisms and immune microenvironment of oxidative stress-related pathways in pancreatic neuroendocrine tumor combining scRNA-seq and bulk RNA

✇(Multiomics OR Omics) AND (Pancreatic)
By: Zhong Cai · Jiangtao Li · Ting Li · Qiuxia Peng
27 January 2026 at 19:00

Discov Oncol. 2026 Jan 27. doi: 10.1007/s12672-026-04515-1. Online ahead of print.

ABSTRACT

BACKGROUND: Pancreatic neuroendocrine tumor (pNET) is a heterogeneous tumor originating from pancreatic endocrine cells. Emerging evidence suggests that oxidative stress plays a crucial role in pNET pathogenesis, yet the precise molecular mechanisms and their interplay with the tumor microenvironment remain unclear. This study aims to systematically elucidate how oxidative stress-related pathways drive pNET progression through an integrated multi-omics approach.

METHODS: We designed a three-tier analytical strategy to address interconnected scientific questions. First, to identify which oxidative stress-related genes are dysregulated in pNET, we performed differential expression analysis and weighted gene co-expression network analysis (WGCNA) on the GSE73338 dataset (63 pNET samples, 5 controls), intersecting the. results with oxidative stress gene sets to obtain 71 candidate genes. Second, to understand the functional implications of these genes, we conducted GO/KEGG enrichment analysis and constructed protein-protein interaction (PPI) networks, from which we identified BCL2L1 and PHGDH as key hub genes using three independent algorithms. We then assessed their diagnostic value through ROC analysis and built a prognostic nomogram model. Third, to explore how these key genes influence the tumor microenvironment, we performed immune infiltration analysis using CIBERSORTx. Fourth, to reveal upstream regulatory mechanisms, we constructed ceRNA networks and predicted transcription factors. Fifth, to identify potential therapeutic interventions, we conducted drug prediction and molecular docking analyses. Finally, to validate our findings at cellular resolution and understand cellular heterogeneity, we analyzed single-cell RNA sequencing data from GSE256136 (20 samples), identifying cell types, quantifying cell-cell communications, and confirming key gene expression patterns across different cell populations.

RESULTS: Our systematic analysis revealed that oxidative stress-related genes in pNET were significantly enriched in the PI3K-Akt signaling pathway, cysteine and methionine metabolism, and HIF-1 signaling pathway. BCL2L1 and PHGDH emerged as central regulators with excellent diagnostic performance (AUC > 0.9). Immune infiltration analysis demonstrated significant alterations in activated dendritic cells, memory B cells, and resting NK cells, which correlated strongly with BCL2L1 and PHGDH expression, suggesting these genes link oxidative stress to immune dysfunction. The ceRNA network centered on KCNQ1OT1 and hsa-miR-15a-5p revealed multi-layered transcriptional and post-transcriptional regulation. Drug prediction identified sertindole and cabozantinib as promising therapeutic candidates. Single-cell analysis identified 11 cell types and confirmed that endocrine cells are the primary site of BCL2L1 and PHGDH dysregulation, with extensive crosstalk between endocrine cells and T cells potentially mediating immune evasion.

CONCLUSION: Through integrated multi-omics analysis, we established that oxidative stress pathways may drive pNET progression through a coordinated mechanism involving metabolic reprogramming (via BCL2L1 and PHGDH downregulation), immune microenvironment remodeling (through altered dendritic cell and NK cell function), and complex regulatory networks. BCL2L1 and PHGDH represent potential diagnostic biomarkers and candidate therapeutic targets that require experimental validation, providing new directions for precision medicine in pNET.

PMID:41591671 | DOI:10.1007/s12672-026-04515-1

Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials

✇npj Digital Medicine
By: Florence D. Mowlem · Jeremiah J. Trudeau · Jill V. Platko · Jos Bloemers · Emuella Flood · Jessica L. Abel · Kelly Dumais · Paul O’Donohoe · Sabrina Grant · Ryan Naville-Cook · Onyekachukwu Illoh · Shannon D. Keith · Jing Ju · Megan Fitter · Dorothee Oberdhan · Randall Winnette · Manuela Bossi · Naomi Suminski · Sonya Eremenco · Lindsay Hughes · Amy Fasiczka · Luisana Rojas · Michelle Campbell · Scottie Kern
27 January 2026 at 08:00

npj Digital Medicine, Published online: 27 January 2026; doi:10.1038/s41746-026-02396-w

Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials

China’s innovation in translational medicine: rethinking early-stage clinical development

✇Nature Biotechnology - Issue - nature.com science feeds
By: Lingshi Tan · Ken Song · Bai Lu
27 January 2026 at 08:00

Nature Biotechnology, Published online: 27 January 2026; doi:10.1038/s41587-025-02998-x

As pressure mounts globally on drug pricing and development cost continues to rise, clinicians and translational scientists in biotech, academia and biopharma companies are re-evaluating when, where and how to launch early clinical programs. These initial patient data become critical to de-risk development programs and allow developers to deploy their limited time and resources on the most promising drugs. We evaluate four fundamental shifts in drug development that appear to be unfolding and may well become critical to future global biopharma success: use of large-scale high quality cohort studies, sponsor-driven investigator-initiated trials, the integration of affordable artificial intelligence with extensive high quality data registries, and China’s focus on precision medicine. —
Received — 27 January 2026 ⏭ Favourites

HyCARD-Net: A Synergistic Hybrid Intelligence Framework for Cardiovascular Disease Diagnosis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rajan Das Gupta · Xiaobin Wu · Xun Liu · Jiaqi He
27 January 2026 at 13:00
arXiv:2601.17767v1 Announce Type: new Abstract: Cardiovascular disease (CVD) remains the foremost cause of mortality worldwide, underscoring the urgent need for intelligent and data-driven diagnostic tools. Traditional predictive models often struggle to generalize across heterogeneous datasets and complex physiological patterns. To address this, we propose a hybrid ensemble framework that integrates deep learning architectures, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), with classical machine learning algorithms, including K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGB), using an ensemble voting mechanism. This approach combines the representational power of deep networks with the interpretability and efficiency of traditional models. Experiments on two publicly available Kaggle datasets demonstrate that the proposed model achieves superior performance, reaching 82.30 percent accuracy on Dataset I and 97.10 percent on Dataset II, with consistent gains in precision, recall, and F1-score. These findings underscore the robustness and clinical potential of hybrid AI frameworks for predicting cardiovascular disease and facilitating early intervention. Furthermore, this study directly supports the United Nations Sustainable Development Goal 3 (Good Health and Well-being) by promoting early diagnosis, prevention, and management of non-communicable diseases through innovative, data-driven healthcare solutions.

Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mehdi Yousefzadeh · Siavash Shirzadeh Barough · Ashkan Fakharifar · Yashar Tayyarazad · Narges Eghbali · Mohaddeseh Mozaffari · Hoda Taeb · Negar Sadat Rafiee Tabatabaee · Parsa Esfahanian · Ghazaleh Sadeghi Gohar · Amineh Safavirad · Saeideh Mazloomzadeh · Ehsan khalilipur · Armin Elahifar · Majid Maleki
27 January 2026 at 13:00
arXiv:2601.17429v1 Announce Type: cross Abstract: X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.

"Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training

✇cs.AI, q-bio.NC updates on arXiv.org
By: David L. Donoho · Jian Kang · Xihong Lin · Bhramar Mukherjee · Dan Nettleton · Rebecca Nugent · Abel Rodriguez · Eric P. Xing · Tian Zheng · Hongtu Zhu
27 January 2026 at 13:00
arXiv:2601.17510v1 Announce Type: cross Abstract: This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future.

GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Maurice Filo · Nicol\`o Rossi · Zhou Fang · Mustafa Khammash
27 January 2026 at 13:00
arXiv:2601.17582v1 Announce Type: cross Abstract: Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics-and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.

From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images

✇cs.AI, q-bio.NC updates on arXiv.org
By: Vi Vu · Thanh-Huy Nguyen · Tien-Thinh Nguyen · Ba-Thinh Lam · Hoang-Thien Nguyen · Tianyang Wang · Xingjian Li · Min Xu
27 January 2026 at 13:00
arXiv:2601.17934v1 Announce Type: cross Abstract: Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and the inability of Parameter-Efficient Fine-Tuning (PEFT) to exploit unlabeled data. While conventional models like U-Net excel in semi-supervised medical learning, their potential to assist a PEFT SAM has been largely overlooked. We introduce SC-SAM, a specialist-generalist framework where U-Net provides point-based prompts and pseudo-labels to guide SAM's adaptation, while SAM serves as a powerful generalist supervisor to regularize U-Net. This reciprocal guidance forms a bidirectional co-training loop that allows both models to effectively exploit the unlabeled data. Across prostate MRI and polyp segmentation benchmarks, our method achieves state-of-the-art results, outperforming other existing semi-supervised SAM variants and even medical foundation models like MedSAM, highlighting the value of specialist-generalist cooperation for label-efficient medical image segmentation. Our code is available at https://github.com/vnlvi2k3/SC-SAM.

The Limits of AI Data Transparency Policy: Three Disclosure Fallacies

✇cs.AI, q-bio.NC updates on arXiv.org
By: Judy Hanwen Shen · Ken Liu · Angelina Wang · Sarah H. Cen · Andy K. Zhang · Caroline Meinhardt · Daniel Zhang · Kevin Klyman · Rishi Bommasani · Daniel E. Ho
27 January 2026 at 13:00
arXiv:2601.18127v1 Announce Type: cross Abstract: Data transparency has emerged as a rallying cry for addressing concerns about AI: data quality, privacy, and copyright chief among them. Yet while these calls are crucial for accountability, current transparency policies often fall short of their intended aims. Similar to nutrition facts for food, policies aimed at nutrition facts for AI currently suffer from a limited consideration of research on effective disclosures. We offer an institutional perspective and identify three common fallacies in policy implementations of data disclosures for AI. First, many data transparency proposals exhibit a specification gap between the stated goals of data transparency and the actual disclosures necessary to achieve such goals. Second, reform attempts exhibit an enforcement gap between required disclosures on paper and enforcement to ensure compliance in fact. Third, policy proposals manifest an impact gap between disclosed information and meaningful changes in developer practices and public understanding. Informed by the social science on transparency, our analysis identifies affirmative paths for transparency that are effective rather than merely symbolic.

Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity

✇cs.AI, q-bio.NC updates on arXiv.org
By: Onyedikachi Hope Amaechi-Okorie · Branislav Radeljic
27 January 2026 at 13:00
arXiv:2601.18641v1 Announce Type: cross Abstract: Speech remains one of the most visible yet overlooked vectors of inclusion and exclusion in contemporary society. While fluency is often equated with credibility and competence, individuals with atypical speech patterns are routinely marginalized. Given the current state of the debate, this article focuses on the structural biases that shape perceptions of atypical speech and are now being encoded into artificial intelligence. Automated speech recognition (ASR) systems and voice interfaces, trained predominantly on standardized speech, routinely fail to recognize or respond to diverse voices, compounding digital exclusion. As AI technologies increasingly mediate access to opportunity, the study calls for inclusive technological design, anti-bias training to minimize the impact of discriminatory algorithmic decisions, and enforceable policy reform that explicitly recognize speech diversity as a matter of equity, not merely accessibility. Drawing on interdisciplinary research, the article advocates for a cultural and institutional shift in how we value voice, urging co-created solutions that elevate the rights, representation, and realities of atypical speakers in the digital age. Ultimately, the article reframes speech inclusion as a matter of equity (not accommodation) and advocates for co-created AI systems that reflect the full spectrum of human voices.

Learning temporal embeddings from electronic health records of chronic kidney disease patients

✇cs.AI, q-bio.NC updates on arXiv.org
By: Aditya Kumar · Mario A. Cypko · Oliver Amft
27 January 2026 at 13:00
arXiv:2601.18675v1 Announce Type: cross Abstract: We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations without compromising predictive performance, and how architectural choices affect embedding quality. Model-guided medicine requires representations that capture disease dynamics while remaining transparent and task agnostic, whereas most clinical prediction models are optimised for a single task. Representation learning facilitates learning embeddings that generalise across downstream tasks, and recurrent architectures are well-suited for modelling temporal structure in observational clinical data. Using the MIMIC-IV dataset, we study patients with chronic kidney disease (CKD) and compare three recurrent architectures: a vanilla LSTM, an attention-augmented LSTM, and a time-aware LSTM (T-LSTM). All models are trained both as embedding models and as direct end-to-end predictors. Embedding quality is evaluated via CKD stage clustering and in-ICU mortality prediction. The T-LSTM produces more structured embeddings, achieving a lower Davies-Bouldin Index (DBI = 9.91) and higher CKD stage classification accuracy (0.74) than the vanilla LSTM (DBI = 15.85, accuracy = 0.63) and attention-augmented LSTM (DBI = 20.72, accuracy = 0.67). For in-ICU mortality prediction, embedding models consistently outperform end-to-end predictors, improving accuracy from 0.72-0.75 to 0.82-0.83, which indicates that learning embeddings as an intermediate step is more effective than direct end-to-end learning.
❌