Application of artificial intelligence in hepatology

By: Zekai Hu · Yajie Lin · Hainv Gao · Leyao Shuai · Zhaowei Tong · Chenqi Nie · Zhe Yang · Junsheng Zhao

1 October 2026 at 18:00

Front Digit Health. 2026 Sep 16;8:1851723. doi: 10.3389/fdgth.2026.1851723. eCollection 2026.

ABSTRACT

Artificial intelligence (AI) is being applied across diagnostic and therapeutic workflows in hepatology. This narrative review summarizes recent advances in AI for liver disease. In medical imaging and digital pathology, computer vision enables automated quantitative analysis of ultrasound, CT, MRI, and histologic images, with the aim of improving the consistency of lesion detection, disease staging, and prognostic assessment. In biomarker research, machine learning can analyze high-dimensional liquid-biopsy and multi-omics data to develop diagnostic and prognostic models; some have outperformed conventional markers in their study cohorts. Electronic health records (EHRs) and large language models (LLMs) are also being investigated for clinical decision support and personalized management. However, most reported evidence remains retrospective, and clinical adoption is limited by data heterogeneity, poor interpretability, uncertain generalizability, and regulatory requirements. Progress will require standardized datasets, external and prospective validation, clinically relevant endpoints, and human-centered implementation before gains in model performance can be translated into better patient outcomes.

PMID:42819088 | PMC:PMC13624904 | DOI:10.3389/fdgth.2026.1851723

SPTLC2-driven sphingolipid reprogramming of neutrophils impairs anti-tumour immunity and drives liver cancer progression

Oncogene - Issue - nature.com science feeds

By: Zhe Yang · Yun Wang · Jianhao Li · Zishuo Wang · Jianan Chen · Ding Zhang · Guorui Zhao · Liwen Liu · Zhibo Liu · Ning Wang · Ranran Sun

28 September 2026 at 08:00

Oncogene, Published online: 28 September 2026; doi:10.1038/s41388-026-03996-2

SPTLC2-driven sphingolipid reprogramming of neutrophils impairs anti-tumour immunity and drives liver cancer progression

DemoEvolve: Overcoming Sparse Feedback in Agentic Harness Evolution with Demonstrations

cs.AI, q-bio.NC updates on arXiv.org

By: Lirong Che · Yuzhe yang · Peiwen lin · Chuang wang · Xueqian wang · Jian su

26 May 2026 at 12:00

arXiv:2605.24539v1 Announce Type: new Abstract: Agent harness evolution improves frozen language-model agents by modifying the executable structures around them. We study this paradigm as a form of sample-efficient fast adaptation: instead of updating model weights, an agent can acquire task-specific competence by changing its external harness, while leaving the base model's general capabilities intact. Prior work shows that self-generated rollouts can support harness search, suggesting that agents may acquire new task competence through practice. Yet in long-horizon stochastic environments, self-practice becomes fragile: rewards are sparse, outcomes are high-variance, and failures are hard to attribute to concrete harness mechanisms. We introduce DemoEvolve, a demonstration-bootstrapped approach to harness evolution. When reward-only search is too broad and noisy, competent human trajectories serve as expert reference experience for the coding proposer, guiding harness-level diagnosis and editing. Experiments on Liar's Dice show that self-rollout evolution can work when episodes are short and failures are attributable. In contrast, Balatro exposes a harder long-horizon stochastic regime, where self-rollout evolution is misled by sparse feedback and candidate-selection noise, while tutorial-like textual knowledge alone does not yield stable improvement. Under the same limited budget, DemoEvolve produces more effective and auditable harness edits and achieves better performance. Overall, demonstrations make sparse-feedback harness evolution more diagnosable, localizable, and stable.

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

cs.AI, q-bio.NC updates on arXiv.org

By: Dongyi He · Bin Jiang · Kecheng Feng · Luyin Zhang · Ling Liu · Yuxuan Li · Yun Zhao · He Yan

26 May 2026 at 12:00

arXiv:2603.03354v3 Announce Type: replace Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet

FileGram: Grounding Agent Personalization in File-System Behavioral Traces

cs.AI, q-bio.NC updates on arXiv.org

By: Shuai Liu · Shulin Tian · Kairui Hu · Yuhao Dong · Zhe Yang · Bo Li · Jingkang Yang · Chen Change Loy · Ziwei Liu

7 April 2026 at 12:00

arXiv:2604.04901v1 Announce Type: cross Abstract: Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human-AI interaction; however, effective personalization remains limited by severe data constraints, as strict privacy barriers and the difficulty of jointly collecting multimodal real-world traces prevent scalable training and evaluation, and existing methods remain interaction-centric while overlooking dense behavioral traces in file-system operations; to address this gap, we propose FileGram, a comprehensive framework that grounds agent memory and personalization in file-system behavioral traces, comprising three core components: (1) FileGramEngine, a scalable persona-driven data engine that simulates realistic workflows and generates fine-grained multimodal action sequences at scale; (2) FileGramBench, a diagnostic benchmark grounded in file-system behavioral traces for evaluating memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding; and (3) FileGramOS, a bottom-up memory architecture that builds user profiles directly from atomic actions and content deltas rather than dialogue summaries, encoding these traces into procedural, semantic, and episodic channels with query-time abstraction; extensive experiments show that FileGramBench remains challenging for state-of-the-art memory systems and that FileGramEngine and FileGramOS are effective, and by open-sourcing the framework, we hope to support future research on personalized memory-centric file-system agents.

SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation

cs.AI, q-bio.NC updates on arXiv.org

By: He Yang · Dongyi Lv · Song Ma · Wei Xi · Jizhong Zhao

1 April 2026 at 12:00

arXiv:2603.28824v1 Announce Type: cross Abstract: Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected into the condensation dataset, manipulating model behavior during inference. While prior approaches have made progress in balancing attack success rate and clean test accuracy, they often fall short in preserving stealthiness, especially in concealing the visual artifacts of condensed data or the perturbations introduced during inference. To address this challenge, we introduce Sneakdoor, which enhances stealthiness without compromising attack effectiveness. Sneakdoor exploits the inherent vulnerability of class decision boundaries and incorporates a generative module that constructs input-aware triggers aligned with local feature geometry, thereby minimizing detectability. This joint design enables the attack to remain imperceptible to both human inspection and statistical detection. Extensive experiments across multiple datasets demonstrate that Sneakdoor achieves a compelling balance among attack success rate, clean test accuracy, and stealthiness, substantially improving the invisibility of both the synthetic data and triggered samples while maintaining high attack efficacy. The code is available at https://github.com/XJTU-AI-Lab/SneakDoor.

LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents

cs.AI, q-bio.NC updates on arXiv.org

By: Zihe Yan · Zhuosheng Zhang · Jiaping Gui · Gongshen Liu

1 April 2026 at 12:00

arXiv:2507.10610v2 Announce Type: replace-cross Abstract: Graphical user interface (GUI) agents built on multimodal large language models (MLLMs) have recently demonstrated strong decision-making abilities in screen-based interaction tasks. However, they remain highly vulnerable to pop-up-based environmental injection attacks, where malicious visual elements divert model attention and lead to unsafe or incorrect actions. Existing defense methods either require costly retraining or perform poorly under inductive interference. In this work, we systematically study how such attacks alter the attention behavior of GUI agents and uncover a layer-wise attention divergence pattern between correct and incorrect outputs. Based on this insight, we propose \textbf{LaSM}, a \textit{Layer-wise Scaling Mechanism} that selectively amplifies attention and MLP modules in critical layers. LaSM improves the alignment between model saliency and task-relevant regions without additional training. Extensive experiments across multiple datasets demonstrate that our method significantly improves the defense success rate and exhibits strong robustness, while having negligible impact on the model's general capabilities. Our findings reveal that attention misalignment is a core vulnerability in MLLM agents and can be effectively addressed through selective layer-wise modulation. Our code can be found in https://github.com/YANGTUOMAO/LaSM.

SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling

cs.AI, q-bio.NC updates on arXiv.org

By: Yiqi Zhang · Huiqiang Jiang · Xufang Luo · Zhihe Yang · Chengruidong Zhang · Yifei Shen · Dongsheng Li · Yuqing Yang · Lili Qiu · Yang You

25 March 2026 at 12:00

arXiv:2603.23414v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) has shown strong promise for enhancing the reasoning abilities of large language models (LLMs), particularly in tasks requiring long chain-of-thought generation. However, RL training efficiency is often bottlenecked by the rollout phase, which can account for up to 70% of total training time when generating long trajectories (e.g., 16k tokens), due to slow autoregressive generation and synchronization overhead between rollout and policy updates. We propose SortedRL, an online length-aware scheduling strategy designed to address this bottleneck by improving rollout efficiency and maintaining training stability. SortedRL reorders rollout samples based on output lengths, prioritizing short samples forming groups for early updates. This enables large rollout batches, flexible update batches, and near on-policy micro-curriculum construction simultaneously. To further accelerate the pipeline, SortedRL incorporates a mechanism to control the degree of off-policy training through a cache-based mechanism, and is supported by a dedicated RL infrastructure that manages rollout and update via a stateful controller and rollout buffer. Experiments using LLaMA-3.1-8B and Qwen-2.5-32B on diverse tasks, including logical puzzles, and math challenges like AIME 24, Math 500, and Minerval, show that SortedRL reduces RL training bubble ratios by over 50%, while attaining 3.9% to 18.4% superior performance over baseline given same amount of data.

Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer

Omics In Lung

By: Pingdong Cao · Xiao Jia · Yuqi Yang · Xi Wang · Jian Zhu · Xiaomei Li · Zhe Yang · Qiang Wen

23 March 2026 at 18:00

J Inflamm Res. 2026 Mar 17;19:581764. doi: 10.2147/JIR.S581764. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have significantly improved the clinical outcomes for patients with non-small cell lung cancer (NSCLC). However, patient heterogeneity and the limitations of current biomarkers contribute to variations in therapeutic responses. Identifying potential beneficiaries of immunotherapy and predicting efficacy remain critical challenges. In recent years, artificial intelligence (AI) has become increasingly applied in cancer treatment, particularly for modeling clinical data and predicting patient prognosis. By integrating multi-omics data such as radiomics, pathomics, genomics, transcriptomics, proteomics, and microbiomics, AI enables comprehensive biomarker discovery and facilitates prediction of immunotherapy responses and potential toxicities in NSCLC patients. Despite these advancements, challenges such as data standardization, limited interpretability, and technical barriers persist. This review summarizes the application of AI in predicting immunotherapy efficacy for NSCLC patients and discusses the challenges and future directions in the context of precision medicine.

PMID:41867453 | PMC:PMC13005593 | DOI:10.2147/JIR.S581764

Insulin resistance prediction from wearables and routine blood biomarkers

Nature - Issue - nature.com science feeds

By: Ahmed A. Metwally · A. Ali Heydari · Daniel McDuff · Alexandru Solot · Zeinab Esmaeilpour · Anthony Z. Faranesh · Menglian Zhou · Girish Narayanswamy · Maxwell A. Xu · Xin Liu · Yuzhe Yang · David B. Savage · Mark Malhotra · Conor Heneghan · Shwetak Patel · Cathy Speed · Javier L. Prieto

16 March 2026 at 08:00

Nature, Published online: 16 March 2026; doi:10.1038/s41586-026-10179-2

A machine-learning model that integrates data from wearable devices (such as smartwatches) with blood biomarkers and demographic data can predict whether someone has insulin resistance, enabling timely lifestyle interventions to prevent progression to type 2 diabetes.

HEARTS: Benchmarking LLM Reasoning on Health Time Series

cs.AI, q-bio.NC updates on arXiv.org

By: Sirui Li · Shuhan Xiao · Mihir Joshi · Ahmed Metwally · Daniel McDuff · Wei Wang · Yuzhe Yang

10 March 2026 at 12:00

arXiv:2603.06638v1 Announce Type: cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of health time series modalities and tasks, failing to reflect the diverse domains and extensive temporal dependencies inherent in real-world physiological modeling. To bridge these gaps, we introduce HEARTS (Health Reasoning over Time Series), a unified benchmark for evaluating hierarchical reasoning capabilities of LLMs over general health time series. HEARTS integrates 16 real-world datasets across 12 health domains and 20 signal modalities, and defines a comprehensive taxonomy of 110 tasks grouped into four core capabilities: Perception, Inference, Generation, and Deduction. Evaluating 14 state-of-the-art LLMs on more than 20K test samples reveals intriguing findings. First, LLMs substantially underperform specialized models, and their performance is only weakly related to general reasoning scores. Moreover, LLMs often rely on simple heuristics and struggle with multi-step temporal reasoning. Finally, performance declines with increasing temporal complexity, with similar failure modes within model families, indicating that scaling alone is insufficient. By making these gaps measurable, HEARTS provides a standardized testbed and living benchmark for developing next-generation LLM agents capable of reasoning over diverse health signals.

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

cs.AI, q-bio.NC updates on arXiv.org

By: Dongyi He · Bin Jiang · Kecheng Feng · Luyin Zhang · Ling Liu · Yuxuan Li · Yun Zhao · He Yan

5 March 2026 at 13:00

arXiv:2603.03354v1 Announce Type: new Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet

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