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Received — 25 February 2026 ⏭ Favourites

AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review

✇Journal of Medical Internet Research
By: Ander Cejudo · Markel Arrojo · Cristina Martín · Aitor Almeida
24 February 2026 at 03:00
Background: Traditional cognitive screening relies on episodic clinical assessments and may miss early changes preceding cognitive impairment and dementia. Wearable and mobile health technologies enable continuous monitoring of sleep, physical activity, and circadian rhythms, generating digital biomarkers that may support scalable early detection and prevention. However, current evidence remains fragmented across devices, analytic approaches, and cognitive outcomes. Objective: This study synthesizes and critically evaluates recent evidence on wearable devices for early detection and prevention of cognitive impairment and dementia, focusing on device categories, cognitive outcomes, analytic approaches, and prevention relevance. Methods: We searched PubMed, Scopus, ACM Digital Library, and SpringerLink for peer-reviewed studies published between January 2020 and December 1, 2025. Eligible studies included human participants with a mean age ≥50 years, continuous wearable-derived data collected for ≥24 hours, and validated cognitive outcomes; reviews, protocols, smartphone-only studies, and pharmacological interventions were excluded. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Appraisal Tool for Cross-Sectional Studies, Newcastle-Ottawa Scale, Cochrane Risk of Bias tool, and Quality Assessment of Diagnostic Accuracy Studies-2. Owing to substantial heterogeneity in devices, outcomes, and analytic methods, quantitative meta-analysis was not feasible; a structured narrative synthesis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidance. This study was not prospectively registered. Results: We included 49 studies, with sample sizes ranging from 14 to 91,948 participants (>200,000 total) and a median sample size of 145. Most used research-grade actigraphy (43/49, 87.8%), while fewer used commercial wearables (7/49, 14.3%). Cognitive outcomes most frequently relied on global screening instruments, including the Mini-Mental State Examination (18/49, 36.7%), followed by ICD-10 (International Statistical Classification of Diseases, Tenth Revision)–based clinical diagnoses (7/49, 14.3%) and the Montreal Cognitive Assessment (7/49, 14.3%). Analytic approaches were predominantly statistical (36/49, 73.5%), with fewer studies applying machine learning (7/49, 14.3%) or deep learning methods (6/49, 12.2%). Statistical analyses linked disrupted sleep, circadian rhythm fragmentation, and irregular activity patterns to worse cognitive outcomes, with modest-to-moderate effect sizes. Machine learning and deep learning approaches reported classification performance with area under the curve values between approximately 0.70 and 0.95. Approximately one-quarter of the studies (13/49, 26.5%) addressed early detection or prevention through longitudinal risk estimation or predictive modeling. Key limitations included small sample sizes, short monitoring durations, and limited external validation. Conclusions: Wearable-derived behavioral markers show promise for early risk stratification. This review advances the field by shifting from descriptive associations toward a digital phenotyping framework evaluating artificial intelligence–driven prediction in the preclinical window. Unlike prior reviews focused on established dementia, it differentiates direct predictive evidence from indirect correlational findings and critically assesses methodological maturity. Continuous, passive monitoring may enable scalable detection of subtle behavioral changes, supporting earlier and more personalized risk reduction strategies. Trial Registration:

Preliminary Exploration of Fluvastatin Inhibiting Proliferation, Migration and Invasion of Lung Cancer Cells and Reversing Paclitaxel Resistance: Mechanism Exploration Based on Multi-Omics Analysis

✇Omics In Lung
By: Hongyu Xu · Zedong Du · Jia Shui · Xueting Li · Juan Tang · Guangquan Li
23 February 2026 at 19:00

Drug Des Devel Ther. 2026 Feb 16;20:579427. doi: 10.2147/DDDT.S579427. eCollection 2026.

ABSTRACT

BACKGROUND: Lung cancer is one of the leading causes of cancer-related deaths, among which NSCLC accounts for approximately 80-85% of all lung cancer cases. Paclitaxel (TAX) is a commonly used chemotherapeutic drug, but it is easy to cause drug resistance. Fluvastatin has anti-cancer potential, but the mechanism of its reversal of drug resistance is unclear.

METHODS: The study was divided into four groups: the A549 control group, the A549/Tax control group, the A549 Fluvastatin-treated group, and the A549/Tax Fluvastatin-treated group. CCK-8, Transwell, and flow cytometry assays were used to detect fluvastatin's effects on cell proliferation, migration, invasion, and apoptosis. Transcriptomics, proteomics, and acetylomics were combined to explore the potential molecular mechanisms.

RESULTS: The preliminary results showed that fluvastatin inhibited the proliferation, migration and invasion of A549 and A549/Tax cells and promoted their apoptosis. Multi-omics analysis revealed that a large number of differentially expressed molecules were detected in both the A549-Fluvastatin vs A549-NC group and the A549/Tax-Fluvastatin vs A549/Tax-NC group, and these molecules were significantly enriched in multiple biological processes and signaling pathways. This suggests that fluvastatin may exert its effects through the synergistic regulation of multiple molecules and pathways. Integrated multi-omics analysis identified several key molecules (for example, HMGCR, RDH11, HSPB1) and acetylated protein-target gene pairs (for example, P09874-BCL2, P42224-PTGS2, P04150-CCND3), which may mediate the antitumor mechanism of fluvastatin.

CONCLUSION: This study indicates that fluvastatin has the potential to reverse TAX resistance in lung cancer, and the results of multi-omics analysis provide a theoretical basis for the exploration of potential therapeutic targets in the future.

PMID:41728357 | PMC:PMC12922964 | DOI:10.2147/DDDT.S579427

Received — 17 February 2026 ⏭ Favourites

MedScope: Incentivizing "Think with Videos" for Clinical Reasoning via Coarse-to-Fine Tool Calling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenjie Li · Yujie Zhang · Haoran Sun · Xingqi He · Hongcheng Gao · Chenglong Ma · Ming Hu · Guankun Wang · Shiyi Yao · Renhao Yang · Hongliang Ren · Lei Wang · Junjun He · Yankai Jiang
17 February 2026 at 13:00
arXiv:2602.13332v1 Announce Type: cross Abstract: Long-form clinical videos are central to visual evidence-based decision-making, with growing importance for applications such as surgical robotics and related settings. However, current multimodal large language models typically process videos with passive sampling or weakly grounded inspection, which limits their ability to iteratively locate, verify, and justify predictions with temporally targeted evidence. To close this gap, we propose MedScope, a tool-using clinical video reasoning model that performs coarse-to-fine evidence seeking over long-form procedures. By interleaving intermediate reasoning with targeted tool calls and verification on retrieved observations, MedScope produces more accurate and trustworthy predictions that are explicitly grounded in temporally localized visual evidence. To address the lack of high-fidelity supervision, we build ClinVideoSuite, an evidence-centric, fine-grained clinical video suite. We then optimize MedScope with Grounding-Aware Group Relative Policy Optimization (GA-GRPO), which directly reinforces tool use with grounding-aligned rewards and evidence-weighted advantages. On full and fine-grained video understanding benchmarks, MedScope achieves state-of-the-art performance in both in-domain and out-of-domain evaluations. Our approach illuminates a path toward medical AI agents that can genuinely "think with videos" through tool-integrated reasoning. We will release our code, models, and data.
Received — 12 February 2026 ⏭ Favourites

Spatial and multi-omics transcriptomic dissects platinum resistance in lung adenocarcinoma: a five-gene predictive model with tumor microenvironment dynamics

✇Omics In Lung
By: Jie Chen · Yixin Chen · Yi Lu · Yuyu He · Feng Jiang · Lijuan Hu · Yumin Wang
9 February 2026 at 19:00

Chem Biol Interact. 2026 Feb 7:111952. doi: 10.1016/j.cbi.2026.111952. Online ahead of print.

ABSTRACT

The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge. This study endeavors to identify molecular subtypes related to platinum resistance and construct a robust predictive model through multi-omics techniques. We performed integrative analysis of public datasets using advanced bioinformatics strategies, including spatial transcriptome deconvolution and consensus clustering. Bulk RNA deconvolution analysis was conducted to characterize tumor microenvironment heterogeneity. Feature selection was performed using the Supervised Principal Component (SuperPC) algorithm, followed by diagnostic model construction validated through receiver operating characteristic (ROC) analysis. Functional validation was performed through cytological experiments measuring cisplatin IC50 alterations following gene manipulation in LUAD cell lines. Consensus clustering revealed distinct LUAD subtypes, with Cluster1 demonstrating significant platinum resistance. We first subtyped the patients in the bulk transcriptome data based on consistency clustering, and then analyzed the differences between different platinum-resistant subtypes (Cluster 1 and Cluster 2), so as to screen 333 isotype-specific differentially expressed genes and 15 platinum resistance-related (PRR) genes were selected through machine learning. A refined 5-gene signature (ANKRD29/CACNA2D2/DSP/HSD17B6/SPP1) achieved exceptional predictive performance (AUC=0.9639). Spatial transcriptomics demonstrated compartmentalized expression patterns: SPP1/DSP localized to tumor niches, HSD17B6/CACNA2D2 to epithelial regions, and ANKRD29 depletion in stromal areas. Cellular colocalization analysis revealed malignant epithelial PH proximity to myeloid and mast cells. Functional validation confirmed that ANKRD29/CACNA2D2 overexpression sensitized A549/DDP cells to cisplatin, while DSP/SPP1/HSD17B6 overexpression induced resistance. Experiments in nude mice have shown that these genes are closely related to cisplatin resistance in LUAD. This study identifies the Cluster1 subtype and malignant epithelial PH as crucial determinants of platinum resistance in LUAD. Our innovative 5-gene predictive model exhibits clinical-grade diagnostic accuracy, and spatial transcriptomic characterization offers mechanistic insights into the dynamics of the tumor microenvironment.

PMID:41662930 | DOI:10.1016/j.cbi.2026.111952

Received — 10 February 2026 ⏭ Favourites

Problems and Barriers Regarding the Admission, Financing, and Service Provision of Digital Health Apps: Qualitative Stakeholder Survey

✇Journal of Medical Internet Research
By: Felix Plescher · Godwin Denk Giebel · Carina Abels · Silke Neusser · Bonnie Kampka · Kirstin Börchers · Jürgen Wasem · Nikola Blase
10 February 2026 at 04:00
Background: Since their introduction with the Digital Care Act in 2019, DiGA are a part of the German statutory healthcare system. In order to become a DiGA, mHealth apps have to complete a certification process covering both technical and evidence related aspects. After completion, DiGA are added to the DiGA-directory, containing a list of all reimbursable DiGA within German statutory health insurance (SHI). The first apps were added at the end of 2020 with the number steadily increasing. The novelty of the introduction leads to problems and barriers to optimal use along the way, which is studied from different stakeholder perspectives in this research article. Objective: The aim of the survey was to identify problems and barriers in the context of certification, financing and use of DiGA in Germany. Methods: We used semi-structured expert interviews to evaluate the perspective of stakeholders of the German healthcare system on DiGA. The interview guide was developed according to Helfferich, the interviews were transcribed and analyzed using the qualitative content approach by Mayring and Kuckartz. Results: We identified problems from stakeholder perspectives regarding the certification/admission, financing and service distribution regarding DiGA. The interviewed stakeholders reported problems with authorization of DiGA and the corresponding process. DiGA prices and the different negotiation positions were criticized, as well as financial challenges for smaller DiGA-manufacturers. Within service provision, technical problems, e. g., with activation codes or software surrounding DiGA-prescription were mentioned. Problems were also seen in insufficient knowledge and skills on the side of the patients as well as the medical providers. Conclusions: mHealth applications provide potentially disruptive innovations within the healthcare sector. Nevertheless, since the evidence-based and regulated use of this technology is relatively new there are still problems and barriers limiting the optimized, patient-centered use. This study provides an overview of problems in the context of DiGA in Germany from the stakeholder perspective. Since other countries showed interest in potentially adopting the German system, valuable implications can be drawn from this survey.

Yunjue Agent Tech Report: A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haotian Li · Shijun Yang · Weizhen Qi · Silei Zhao · Rui Hua · Mingzhu Song · Xiaojian Yang · Chao Peng
9 February 2026 at 13:00
arXiv:2601.18226v2 Announce Type: replace Abstract: Conventional agent systems often struggle in open-ended environments where task distributions continuously drift and external supervision is scarce. Their reliance on static toolsets or offline training lags behind these dynamics, leaving the system's capability boundaries rigid and unknown. To address this, we propose the In-Situ Self-Evolving paradigm. This approach treats sequential task interactions as a continuous stream of experience, enabling the system to distill short-term execution feedback into long-term, reusable capabilities without access to ground-truth labels. Within this framework, we identify tool evolution as the critical pathway for capability expansion, which provides verifiable, binary feedback signals. Within this framework, we develop Yunjue Agent, a system that iteratively synthesizes, optimizes, and reuses tools to navigate emerging challenges. To optimize evolutionary efficiency, we further introduce a Parallel Batch Evolution strategy. Empirical evaluations across five diverse benchmarks under a zero-start setting demonstrate significant performance gains over proprietary baselines. Additionally, complementary warm-start evaluations confirm that the accumulated general knowledge can be seamlessly transferred to novel domains. Finally, we propose a novel metric to monitor evolution convergence, serving as a function analogous to training loss in conventional optimization. We open-source our codebase, system traces, and evolved tools to facilitate future research in resilient, self-evolving intelligence.

Reliability of LLMs as medical assistants for the general public: a randomized preregistered study

✇Nature Medicine
By: Andrew M. Bean · Rebecca Elizabeth Payne · Guy Parsons · Hannah Rose Kirk · Juan Ciro · Rafael Mosquera-Gómez · Sara Hincapié M · Aruna S. Ekanayaka · Lionel Tarassenko · Luc Rocher · Adam Mahdi
9 February 2026 at 08:00

Nature Medicine, Published online: 09 February 2026; doi:10.1038/s41591-025-04074-y

In a randomized controlled study involving 1,298 participants from a general sample, performance of humans when assisted by a large language model (LLM) was sensibly inferior to that of the LLM alone when assessing ten medical scenarios leading to disease identification and recommendations for treatment.

EcDNA-borne structural variants drive oncogenic fusion transcript amplification

✇Cell
By: Hyerim Yi · Shu Zhang · Jason Swinderman · Yanbo Wang · Vishnupriya Kanakaveti · King L. Hung · Ivy Tsz-Lo Wong · Suhas Srinivasan · Ellis J. Curtis · Aarohi Bhargava-Shah · Rui Li · Matthew G. Jones · Jens Luebeck · Chris Bailey · Yanding Zhao · Julia A. Belk · Katerina Kraft · Quanming Shi · Xiaowei Yan · Simon K. Pritchard · Kabir S. Mahajan · Frances Liang · Mariam Jamal-Hanjani · Dean W. Felsher · Luke A. Gilbert · Vineet Bafna · Paul S. Mischel · Howard Y. Chang
7 January 2026 at 08:00
Extrachromosomal DNA (ecDNA) is a major source of oncogenic fusions across cancer types, generating tissue-specific fusion landscapes with diagnostic potential. EcDNA-borne PVT1 5′-end fusions stabilize partner RNAs and boost oncogene output.
Received — 5 February 2026 ⏭ Favourites

From Data to Behavior: Predicting Unintended Model Behaviors Before Training

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mengru Wang · Zhenqian Xu · Junfeng Fang · Yunzhi Yao · Shumin Deng · Huajun Chen · Ningyu Zhang
5 February 2026 at 13:00
arXiv:2602.04735v1 Announce Type: cross Abstract: Large Language Models (LLMs) can acquire unintended biases from seemingly benign training data even without explicit cues or malicious content. Existing methods struggle to detect such risks before fine-tuning, making post hoc evaluation costly and inefficient. To address this challenge, we introduce Data2Behavior, a new task for predicting unintended model behaviors prior to training. We also propose Manipulating Data Features (MDF), a lightweight approach that summarizes candidate data through their mean representations and injects them into the forward pass of a base model, allowing latent statistical signals in the data to shape model activations and reveal potential biases and safety risks without updating any parameters. MDF achieves reliable prediction while consuming only about 20% of the GPU resources required for fine-tuning. Experiments on Qwen3-14B, Qwen2.5-32B-Instruct, and Gemma-3-12b-it confirm that MDF can anticipate unintended behaviors and provide insight into pre-training vulnerabilities.
Received — 4 February 2026 ⏭ Favourites

Extrachromosomal DNA drives molecular and clinical heterogeneity in hepatocellular carcinoma: a multi-omics analysis and prognostic model development

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Kai Huang · Guangquan Zhang · Shuai Hu · Yongfei He · Hang Zhai · Junming Xu · Jicai Wang · Shengjie Hong · Fenfang Wu · Xianjie Shi
4 February 2026 at 19:00

Hum Genomics. 2026 Feb 3. doi: 10.1186/s40246-026-00927-w. Online ahead of print.

ABSTRACT

BACKGROUND: Extrachromosomal DNA (ecDNA) is an emerging hallmark of cancer that promotes tumor evolution and heterogeneity. However, the molecular characteristics and clinical significance of ecDNA in hepatocellular carcinoma (HCC) remain incompletely understood.

METHODS: The clinical outcomes, genomics, transcriptomics, proteomics, tumor microenvironment, and drug target landscapes of ecDNA-negative and ecDNA-positive HCC in the Cancer Genome Atlas (TCGA) were compared. Next, the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF) algorithms were used to screen the ecDNA gene signature. A nomogram was constructed and evaluated based on the risk score and clinicopathological features. Finally, the role of DNASE1L3 was validated through in vitro experiments.

RESULTS: EcDNA-positive tumors showed increased vascular invasion, higher AFP levels, and more TP53 mutations. These tumors displayed unique activation of proliferation pathways, decreased stromal infiltration, and heightened immune activation. Our validated six-gene signature (RNF186, BMP6, AOC1, FBLL1, MYBL2, and DNASE1L3) demonstrated strong prognostic value when combined with tumor stage in the nomogram. Notably, DNASE1L3 was downregulated in HCC, showed endothelial cell-specific expression, and suppressed the proliferation and migration of Hep3B2.1-7 cells.

CONCLUSION: Our study characterizes the molecular and clinical distinctions between ecDNA-negative and ecDNA-positive HCC and establishes a clinically applicable gene signature for patient prognosis. These findings advance our understanding of ecDNA-driven tumor heterogeneity and provide potential strategies for personalized HCC management.

PMID:41634868 | DOI:10.1186/s40246-026-00927-w

Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer

✇Omics in Gastric
By: Ya-Hsuan Chang · Tzu-Chan Hong · Kuen-Tyng Lin · Yi-Jing Hsiao · Hsiang-En Hsu · Juanilita T Waniwan · Rodrigo Espinoza Silva · I-Rue Lai · Po-Chu Lee · Ming-Tsan Lin · Chia-Tung Shun · Min-Shu Hsieh · Yi-Ju Chen · Song-Wei Wang · Wen-Hung Hsu · I-Chen Wu · Yao-Kuang Wang · Ching-Chun Li · Jaw-Yuan Wang · Yin-Chen Hsu · Hao Fang · Ze-Shiang Lin · Wen-Hsin Chang · Jyun-Hong Lin · Yan-Si Chen · Ying-Chieh Ko · Chi-Ya Shen · Yan-Ming Chen · Chia-Yu Wang · Yu-Teng Jheng · Wen-Yi Liu · Yu-Tai Wang · Chang-Wei Yeh · Pei-Rong Huang · Jyh-Ming Liou · Li-Tzong Chen · Chia-Li Han · Deng-Chyang Wu · Hsuan-Yu Chen · Sung-Liang Yu · Ming-Shiang Wu · Yu-Ju Chen · Taiwan Cancer Moonshot Program
30 January 2026 at 19:00

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

Received — 28 January 2026 ⏭ Favourites

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
Received — 27 January 2026 ⏭ Favourites

Federated Proximal Optimization for Privacy-Preserving Heart Disease Prediction: A Controlled Simulation Study on Non-IID Clinical Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Farzam Asad · Junaid Saif Khan · Maria Tariq · Sundus Munir · Muhammad Adnan Khan
27 January 2026 at 13:00
arXiv:2601.17183v1 Announce Type: cross Abstract: Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy regulations like HIPAA and GDPR prevent hospitals from directly sharing data with one another. Federated Learning offers a way out to this problem by facilitating collaborative model training without having the raw patient data centralized. However, clinical datasets intrinsically have non-IID (non-independent and identically distributed) features brought about by demographic disparity and diversity in disease prevalence and institutional practices. This paper presents a comprehensive simulation research of Federated Proximal Optimization (FedProx) for Heart Disease prediction based on UCI Heart Disease dataset. We generate realistic non-IID data partitions by simulating four heterogeneous hospital clients from the Cleveland Clinic dataset (303 patients), by inducing statistical heterogeneity by demographic-based stratification. Our experimental results show that FedProx with proximal parameter mu=0.05 achieves 85.00% accuracy, which is better than both centralized learning (83.33%) and isolated local models (78.45% average) without revealing patient privacy. Through generous sheer ablation studies with statistical validation on 50 independent runs we demonstrate that proximal regularization is effective in curbing client drift in heterogeneous environments. This proof-of-concept research offers algorithmic insights and practical deployment guidelines for real-world federated healthcare systems, and thus, our results are directly transferable to hospital IT-administrators, implementing privacy-preserving collaborative learning.

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.

Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer

✇npj Digital Medicine
By: Sheng Chen · Ping’an Ding · Yihao Yang · Shuo Ma · Honghai Guo · Xiao Han · Jiaxuan Yang · Wenqian Ma · Ning Meng · Zhijia Xia · Xiaolong Li · Lilong Zhang · Yanlong Shi · Zhenjiang Guo · Kaixuan Gao · Renjun Gu · Hong Long · Lingjiao Meng · Qun Zhao
26 January 2026 at 08:00

npj Digital Medicine, Published online: 26 January 2026; doi:10.1038/s41746-025-02268-9

Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer

PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: John Wu · Yongda Fan · Zhenbang Wu · Paul Landes · Eric Schrock · Sayeed Sajjad Razin · Arjun Chatterjee · Naveen Baskaran · Joshua Steier · Andrea Fitzpatrick · Bilal Arif · Rian Atri · Jathurshan Pradeepkumar · Siddhartha Laghuvarapu · Junyi Gao · Adam R. Cross · Jimeng Sun
26 January 2026 at 13:00
arXiv:2601.16414v1 Announce Type: cross Abstract: Difficulty replicating baselines, high computational costs, and required domain expertise create persistent barriers to clinical AI research. To address these challenges, we introduce PyHealth 2.0, an enhanced clinical deep learning toolkit that enables predictive modeling in as few as 7 lines of code. PyHealth 2.0 offers three key contributions: (1) a comprehensive toolkit addressing reproducibility and compatibility challenges by unifying 15+ datasets, 20+ clinical tasks, 25+ models, 5+ interpretability methods, and uncertainty quantification including conformal prediction within a single framework that supports diverse clinical data modalities - signals, imaging, and electronic health records - with translation of 5+ medical coding standards; (2) accessibility-focused design accommodating multimodal data and diverse computational resources with up to 39x faster processing and 20x lower memory usage, enabling work from 16GB laptops to production systems; and (3) an active open-source community of 400+ members lowering domain expertise barriers through extensive documentation, reproducible research contributions, and collaborations with academic health systems and industry partners, including multi-language support via RHealth. PyHealth 2.0 establishes an open-source foundation and community advancing accessible, reproducible healthcare AI. Available at pip install pyhealth.

DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haotian Chen · Qingqing Long · Siyu Pu · Xiao Luo · Wei Ju · Meng Xiao · Yuanchun Zhou · Jianghua Zhao · Xuezhi Wang
26 January 2026 at 13:00
arXiv:2601.16478v1 Announce Type: cross Abstract: With the rapid growth of scientific literature, scientific question answering (SciQA) has become increasingly critical for exploring and utilizing scientific knowledge. Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating knowledge from external sources, thereby providing credible evidence for scientific question answering. But existing retrieval and reranking methods remain vulnerable to passages that are semantically similar but logically irrelevant, often reducing factual reliability and amplifying hallucinations.To address this challenge, we propose a Deep Evidence Reranking Agent (DeepEra) that integrates step-by-step reasoning, enabling more precise evaluation of candidate passages beyond surface-level semantics. To support systematic evaluation, we construct SciRAG-SSLI (Scientific RAG - Semantically Similar but Logically Irrelevant), a large-scale dataset comprising about 300K SciQA instances across 10 subjects, constructed from 10M scientific corpus. The dataset combines naturally retrieved contexts with systematically generated distractors to test logical robustness and factual grounding. Comprehensive evaluations confirm that our approach achieves superior retrieval performance compared to leading rerankers. To our knowledge, this work is the first to comprehensively study and empirically validate innegligible SSLI issues in two-stage RAG frameworks.
Received — 23 January 2026 ⏭ Favourites

An Optimized Decision Tree-Based Framework for Explainable IoT Anomaly Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ashikuzzaman · Md. Shawkat Hossain · Jubayer Abdullah Joy · Md Zahid Akon · Md Manjur Ahmed · Md. Naimul Islam
22 January 2026 at 13:00
arXiv:2601.14305v1 Announce Type: cross Abstract: The increase in the number of Internet of Things (IoT) devices has tremendously increased the attack surface of cyber threats thus making a strong intrusion detection system (IDS) with a clear explanation of the process essential towards resource-constrained environments. Nevertheless, current IoT IDS systems are usually traded off with detection quality, model elucidability, and computational effectiveness, thus the deployment on IoT devices. The present paper counteracts these difficulties by suggesting an explainable AI (XAI) framework based on an optimized Decision Tree classifier with both local and global importance methods: SHAP values that estimate feature attribution using local explanations, and Morris sensitivity analysis that identifies the feature importance in a global view. The proposed system attains the state of art on the test performance with 99.91% accuracy, F1-score of 99.51% and Cohen Kappa of 0.9960 and high stability is confirmed by a cross validation mean accuracy of 98.93%. Efficiency is also enhanced in terms of computations to provide faster inferences compared to those that are generalized in ensemble models. SrcMac has shown as the most significant predictor in feature analyses according to SHAP and Morris methods. Compared to the previous work, our solution eliminates its major drawback lack because it allows us to apply it to edge devices and, therefore, achieve real-time processing, adhere to the new regulation of transparency in AI, and achieve high detection rates on attacks of dissimilar classes. This combination performance of high accuracy, explainability, and low computation make the framework useful and reliable as a resource-constrained IoT security problem in real environments.
Received — 22 January 2026 ⏭ Favourites
Received — 21 January 2026 ⏭ Favourites

OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ming Zhang · Kexin Tan · Yueyuan Huang · Yujiong Shen · Chunchun Ma · Li Ju · Xinran Zhang · Yuhui Wang · Wenqing Jing · Jingyi Deng · Huayu Sha · Binze Hu · Jingqi Tong · Changhao Jiang · Yage Geng · Yuankai Ying · Yue Zhang · Zhangyue Yin · Zhiheng Xi · Shihan Dou · Tao Gui · Qi Zhang · Xuanjing Huang
21 January 2026 at 13:00
arXiv:2601.01576v2 Announce Type: replace-cross Abstract: Evaluating novelty is critical yet challenging in peer review, as reviewers must assess submissions against a vast, rapidly evolving literature. This report presents OpenNovelty, an LLM-powered agentic system for transparent, evidence-based novelty analysis. The system operates through four phases: (1) extracting the core task and contribution claims to generate retrieval queries; (2) retrieving relevant prior work based on extracted queries via semantic search engine; (3) constructing a hierarchical taxonomy of core-task-related work and performing contribution-level full-text comparisons against each contribution; and (4) synthesizing all analyses into a structured novelty report with explicit citations and evidence snippets. Unlike naive LLM-based approaches, \textsc{OpenNovelty} grounds all assessments in retrieved real papers, ensuring verifiable judgments. We deploy our system on 500+ ICLR 2026 submissions with all reports publicly available on our website, and preliminary analysis suggests it can identify relevant prior work, including closely related papers that authors may overlook. OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review.
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