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Received — 31 October 2025 ⏭ Favourites

An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jorge Martinez-Gil · Mario Pichler · Nefeli Bountouni · Sotiris Koussouris · Marielena M\'arquez Barreiro · Sergio Gusmeroli
31 October 2025 at 12:00
arXiv:2510.25813v1 Announce Type: new Abstract: We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, or collaborative, are responsible for well-defined tasks, enabling flexibility and simplifying integration. Moreover, our framework supports modular integration and maintains low resource requirements. Preliminary evaluations concerning the food industry in real scenarios indicate improved deployment time and system adaptability performance. The source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component.

Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Reda El Makroum · Sebastian Zwickl-Bernhard · Lukas Kranzl
31 October 2025 at 12:00
arXiv:2510.26603v1 Announce Type: new Abstract: The electricity sector transition requires substantial increases in residential demand response capacity, yet Home Energy Management Systems (HEMS) adoption remains limited by user interaction barriers requiring translation of everyday preferences into technical parameters. While large language models have been applied to energy systems as code generators and parameter extractors, no existing implementation deploys LLMs as autonomous coordinators managing the complete workflow from natural language input to multi-appliance scheduling. This paper presents an agentic AI HEMS where LLMs autonomously coordinate multi-appliance scheduling from natural language requests to device control, achieving optimal scheduling without example demonstrations. A hierarchical architecture combining one orchestrator with three specialist agents uses the ReAct pattern for iterative reasoning, enabling dynamic coordination without hardcoded workflows while integrating Google Calendar for context-aware deadline extraction. Evaluation across three open-source models using real Austrian day-ahead electricity prices reveals substantial capability differences. Llama-3.3-70B successfully coordinates all appliances across all scenarios to match cost-optimal benchmarks computed via mixed-integer linear programming, while other models achieve perfect single-appliance performance but struggle to coordinate all appliances simultaneously. Progressive prompt engineering experiments demonstrate that analytical query handling without explicit guidance remains unreliable despite models' general reasoning capabilities. We open-source the complete system including orchestration logic, agent prompts, tools, and web interfaces to enable reproducibility, extension, and future research.

Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tobin South · Subramanya Nagabhushanaradhya · Ayesha Dissanayaka · Sarah Cecchetti · George Fletcher · Victor Lu · Aldo Pietropaolo · Dean H. Saxe · Jeff Lombardo · Abhishek Maligehalli Shivalingaiah · Stan Bounev · Alex Keisner · Andor Kesselman · Zack Proser · Ginny Fahs · Andrew Bunyea · Ben Moskowitz · Atul Tulshibagwale · Dazza Greenwood · Jiaxin Pei · Alex Pentland
31 October 2025 at 12:00
arXiv:2510.25819v1 Announce Type: cross Abstract: The rapid rise of AI agents presents urgent challenges in authentication, authorization, and identity management. Current agent-centric protocols (like MCP) highlight the demand for clarified best practices in authentication and authorization. Looking ahead, ambitions for highly autonomous agents raise complex long-term questions regarding scalable access control, agent-centric identities, AI workload differentiation, and delegated authority. This OpenID Foundation whitepaper is for stakeholders at the intersection of AI agents and access management. It outlines the resources already available for securing today's agents and presents a strategic agenda to address the foundational authentication, authorization, and identity problems pivotal for tomorrow's widespread autonomous systems.

Multi-Agent Reinforcement Learning for Market Making: Competition without Collusion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyi Wang · Carmine Ventre · Maria Polukarov
31 October 2025 at 12:00
arXiv:2510.25929v1 Announce Type: cross Abstract: Algorithmic collusion has emerged as a central question in AI: Will the interaction between different AI agents deployed in markets lead to collusion? More generally, understanding how emergent behavior, be it a cartel or market dominance from more advanced bots, affects the market overall is an important research question. We propose a hierarchical multi-agent reinforcement learning framework to study algorithmic collusion in market making. The framework includes a self-interested market maker (Agent~A), which is trained in an uncertain environment shaped by an adversary, and three bottom-layer competitors: the self-interested Agent~B1 (whose objective is to maximize its own PnL), the competitive Agent~B2 (whose objective is to minimize the PnL of its opponent), and the hybrid Agent~B$^\star$, which can modulate between the behavior of the other two. To analyze how these agents shape the behavior of each other and affect market outcomes, we propose interaction-level metrics that quantify behavioral asymmetry and system-level dynamics, while providing signals potentially indicative of emergent interaction patterns. Experimental results show that Agent~B2 secures dominant performance in a zero-sum setting against B1, aggressively capturing order flow while tightening average spreads, thus improving market execution efficiency. In contrast, Agent~B$^\star$ exhibits a self-interested inclination when co-existing with other profit-seeking agents, securing dominant market share through adaptive quoting, yet exerting a milder adverse impact on the rewards of Agents~A and B1 compared to B2. These findings suggest that adaptive incentive control supports more sustainable strategic co-existence in heterogeneous agent environments and offers a structured lens for evaluating behavioral design in algorithmic trading systems.

Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihe Deng · I-Hung Hsu · Jun Yan · Zifeng Wang · Rujun Han · Gufeng Zhang · Yanfei Chen · Wei Wang · Tomas Pfister · Chen-Yu Lee
31 October 2025 at 12:00
arXiv:2510.25992v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLVR) fails when correct solutions are rarely sampled even after many attempts, while Supervised Fine-Tuning (SFT) tends to overfit long demonstrations through rigid token-by-token imitation. To address this gap, we propose Supervised Reinforcement Learning (SRL), a framework that reformulates problem solving as generating a sequence of logical "actions". SRL trains the model to generate an internal reasoning monologue before committing to each action. It provides smoother rewards based on the similarity between the model's actions and expert actions extracted from the SFT dataset in a step-wise manner. This supervision offers richer learning signals even when all rollouts are incorrect, while encouraging flexible reasoning guided by expert demonstrations. As a result, SRL enables small models to learn challenging problems previously unlearnable by SFT or RLVR. Moreover, initializing training with SRL before refining with RLVR yields the strongest overall performance. Beyond reasoning benchmarks, SRL generalizes effectively to agentic software engineering tasks, establishing it as a robust and versatile training framework for reasoning-oriented LLMs.

The Quest for Reliable Metrics of Responsible AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Theresia Veronika Rampisela · Maria Maistro · Tuukka Ruotsalo · Christina Lioma
31 October 2025 at 12:00
arXiv:2510.26007v1 Announce Type: cross Abstract: The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quantified through evaluation metrics, yet there has been less work on assessing the robustness and reliability of the metrics themselves. We reflect on prior work that examines the robustness of fairness metrics for recommender systems as a type of AI application and summarise their key takeaways into a set of non-exhaustive guidelines for developing reliable metrics of responsible AI. Our guidelines apply to a broad spectrum of AI applications, including AIS.

A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Domenico Amalfitano · Andreas Metzger · Marco Autili · Tommaso Fulcini · Tobias Hey · Jan Keim · Patrizio Pelliccione · Vincenzo Scotti · Anne Koziolek · Raffaela Mirandola · Andreas Vogelsang
31 October 2025 at 12:00
arXiv:2510.26275v1 Announce Type: cross Abstract: Generative AI (GenAI) is rapidly transforming software engineering (SE) practices, influencing how SE processes are executed, as well as how software systems are developed, operated, and evolved. This paper applies design science research to build a roadmap for GenAI-augmented SE. The process consists of three cycles that incrementally integrate multiple sources of evidence, including collaborative discussions from the FSE 2025 "Software Engineering 2030" workshop, rapid literature reviews, and external feedback sessions involving peers. McLuhan's tetrads were used as a conceptual instrument to systematically capture the transforming effects of GenAI on SE processes and software products.The resulting roadmap identifies four fundamental forms of GenAI augmentation in SE and systematically characterizes their related research challenges and opportunities. These insights are then consolidated into a set of future research directions. By grounding the roadmap in a rigorous multi-cycle process and cross-validating it among independent author teams and peers, the study provides a transparent and reproducible foundation for analyzing how GenAI affects SE processes, methods and tools, and for framing future research within this rapidly evolving area. Based on these findings, the article finally makes ten predictions for SE in the year 2030.

From Amateur to Master: Infusing Knowledge into LLMs via Automated Curriculum Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Nishit Neema · Srinjoy Mukherjee · Sapan Shah · Gokul Ramakrishnan · Ganesh Venkatesh
31 October 2025 at 12:00
arXiv:2510.26336v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at general tasks but underperform in specialized domains like economics and psychology, which require deep, principled understanding. To address this, we introduce ACER (Automated Curriculum-Enhanced Regimen) that transforms generalist models into domain experts without sacrificing their broad capabilities. ACER first synthesizes a comprehensive, textbook-style curriculum by generating a table of contents for a subject and then creating question-answer (QA) pairs guided by Bloom's taxonomy. This ensures systematic topic coverage and progressively increasing difficulty. The resulting synthetic corpus is used for continual pretraining with an interleaved curriculum schedule, aligning learning across both content and cognitive dimensions. Experiments with Llama 3.2 (1B and 3B) show significant gains in specialized MMLU subsets. In challenging domains like microeconomics, where baselines struggle, ACER boosts accuracy by 5 percentage points. Across all target domains, we observe a consistent macro-average improvement of 3 percentage points. Notably, ACER not only prevents catastrophic forgetting but also facilitates positive cross-domain knowledge transfer, improving performance on non-target domains by 0.7 points. Beyond MMLU, ACER enhances performance on knowledge-intensive benchmarks like ARC and GPQA by over 2 absolute points, while maintaining stable performance on general reasoning tasks. Our results demonstrate that ACER offers a scalable and effective recipe for closing critical domain gaps in LLMs.

Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xi Li · Ruofan Mao · Yusen Zhang · Renze Lou · Chen Wu · Jiaqi Wang
31 October 2025 at 12:00
arXiv:2406.05948v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs), especially those accessed via APIs, have demonstrated impressive capabilities across various domains. However, users without technical expertise often turn to (untrustworthy) third-party services, such as prompt engineering, to enhance their LLM experience, creating vulnerabilities to adversarial threats like backdoor attacks. Backdoor-compromised LLMs generate malicious outputs to users when inputs contain specific "triggers" set by attackers. Traditional defense strategies, originally designed for small-scale models, are impractical for API-accessible LLMs due to limited model access, high computational costs, and data requirements. To address these limitations, we propose Chain-of-Scrutiny (CoS) which leverages LLMs' unique reasoning abilities to mitigate backdoor attacks. It guides the LLM to generate reasoning steps for a given input and scrutinizes for consistency with the final output -- any inconsistencies indicating a potential attack. It is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data. User-friendly and driven by natural language, it allows non-experts to perform the defense independently while maintaining transparency. We validate the effectiveness of CoS through extensive experiments on various tasks and LLMs, with results showing greater benefits for more powerful LLMs.

Epistemic Diversity and Knowledge Collapse in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dustin Wright · Sarah Masud · Jared Moore · Srishti Yadav · Maria Antoniak · Chan Young Park · Isabelle Augenstein
31 October 2025 at 12:00
arXiv:2510.04226v4 Announce Type: replace-cross Abstract: Large language models (LLMs) tend to generate lexically, semantically, and stylistically homogenous texts. This poses a risk of knowledge collapse, where homogenous LLMs mediate a shrinking in the range of accessible information over time. Existing works on homogenization are limited by a focus on closed-ended multiple-choice setups or fuzzy semantic features, and do not look at trends across time and cultural contexts. To overcome this, we present a new methodology to measure epistemic diversity, i.e., variation in real-world claims in LLM outputs, which we use to perform a broad empirical study of LLM knowledge collapse. We test 27 LLMs, 155 topics covering 12 countries, and 200 prompt variations sourced from real user chats. For the topics in our study, we show that while newer models tend to generate more diverse claims, nearly all models are less epistemically diverse than a basic web search. We find that model size has a negative impact on epistemic diversity, while retrieval-augmented generation (RAG) has a positive impact, though the improvement from RAG varies by the cultural context. Finally, compared to a traditional knowledge source (Wikipedia), we find that country-specific claims reflect the English language more than the local one, highlighting a gap in epistemic representation

Integrating Genomics into Multimodal EHR Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jonathan Amar · Edward Liu · Alessandra Breschi · Liangliang Zhang · Pouya Kheradpour · Sylvia Li · Lisa Soleymani Lehmann · Alessandro Giulianelli · Matt Edwards · Yugang Jia · David Nola · Raghav Mani · Pankaj Vats · Jesse Tetreault · T. J. Chen · Cory Y. McLean
31 October 2025 at 12:00
arXiv:2510.23639v2 Announce Type: replace-cross Abstract: This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.

Toward governance of artificial intelligence in pediatric healthcare

✇npj Digital Medicine
By: Felix Richter · Emma Holmes · Florian Richter · Katherine Guttmann · Son Q. Duong · Sandeep Gangadharan · Eric E. Schadt · Hojjat Salmasian · Bruce D. Gelb · Benjamin S. Glicksberg
30 October 2025 at 08:00

npj Digital Medicine, Published online: 30 October 2025; doi:10.1038/s41746-025-02000-7

Toward governance of artificial intelligence in pediatric healthcare
Received — 30 October 2025 ⏭ Favourites

Multi-omic profiling reveals age-related immune dynamics in healthy adults

✇Nature - Issue - nature.com science feeds
By: Qiuyu Gong · Mehul Sharma · Marla C. Glass · Emma L. Kuan · Aishwarya Chander · Mansi Singh · Lucas T. Graybuck · Zachary J. Thomson · Christian M. LaFrance · Samir Rachid Zaim · Tao Peng · Lauren Y. Okada · Palak C. Genge · Katherine E. Henderson · Elisabeth M. Dornisch · Erik D. Layton · Peter J. Wittig · Alexander T. Heubeck · Nelson M. Mukuka · Julian Reading · Garrett Strawn · Teminijesu Titus-Adewunmi · Kathleen Abadie · Charles R. Roll · Veronica Hernandez · Vaishnavi Parthasarathy · Tyanna J. Stuckey · Blessing Musgrove · Elliott Swanson · Cara Lord · Morgan D. A. Weiss · Cole G. Phalen · Regina R. Mettey · Kevin J. Lee · John B. Johanneson · Erin K. Kawelo · Jessica Garber · Upaasana Krishnan · Megan Smithmyer · E. John Wherry · Laura A. Vella · Sarah E. Henrickson · Mackenzie S. Kopp · Adam K. Savage · Lynne A. Becker · Paul Meijer · Ernest M. Coffey · Jorg J. Goronzy · Mikael Sigvardsson · Cate Speake · Thomas F. Bumol · Ananda W. Goldrath · Troy R. Torgerson · Xiao-jun Li · Peter J. Skene · Jane H. Buckner · Claire E. Gustafson
29 October 2025 at 08:00

Nature, Published online: 29 October 2025; doi:10.1038/s41586-025-09686-5

This multi-omic longitudinal analysis of the healthy human peripheral immune system constructs the Human Immune Health Atlas and assembles data on immune cell composition and state changes with age, including responses to cytomegalovirus infection and influenza vaccination.

Advancing Non-Small-Cell Lung Cancer Management Through Multi-Omics Integration: Insights from Genomics, Metabolomics, and Radiomics

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Martina Pierri · Giovanni Ciani · Maria Chiara Brunese · Gianluigi Lauro · Stefania Terracciano · Maria Iorizzi · Valerio Nardone · Maria Giovanna Chini · Giuseppe Bifulco · Salvatore Cappabianca · Alfonso Reginelli
29 October 2025 at 18:00

Diagnostics (Basel). 2025 Oct 14;15(20):2586. doi: 10.3390/diagnostics15202586.

ABSTRACT

The integration of multi-omics technologies is transforming the landscape of cancer management, offering unprecedented insights into tumor biology, early diagnosis, and personalized therapy. This review provides a comprehensive overview of the current state of omics approaches, with a particular focus on the application of genomics, NMR-based metabolomics, and radiomics in non-small cell lung cancer (NSCLC). Genomics currently represents one of the most established omics technologies in oncology, as it enables the identification of genetic alterations that drive tumor initiation, progression, and therapeutic response. Interestingly, genomic analyses have revealed that many tumors harbor mutations in genes encoding metabolic enzymes, thus establishing a tight connection between genomics and tumor metabolism. In parallel, metabolomics profiling-by capturing the metabolic phenotype of tumors-has, in recent years, identified specific biomarkers associated with tumor burden, progression, and prognosis. Such findings have catalyzed growing interest in metabolomics as a complementary approach to better characterize cancer biology and discover novel diagnostic and therapeutic targets. Moreover, radiomics, through the extraction of quantitative features from standard imaging modalities, captures tumor heterogeneity and contributes predictive information on tumor biology, treatment response, and clinical outcomes. As a non-invasive and widely available technique, radiomics has the potential to support longitudinal monitoring and individualized treatment planning. Both metabolomics and radiomics, when integrated with genomic data, could support a more comprehensive understanding of NSCLC and pave the way for the development of non-invasive, predictive models and personalized therapeutic strategies. In addition, we explore the specific contributions of these technologies in enhancing clinical decision-making for lung cancer patients, with particular attention to their potential in early diagnosis, treatment selection, and real-time monitoring.

PMID:41153258 | DOI:10.3390/diagnostics15202586

Improving Recruitment Into Research Studies via Electronically Collected Patient-Entered Data: Mixed Methods Study

✇Journal of Medical Internet Research
By: Irene Katzan · WH Wilson Tang · Andrew Schuster · Ryan Honomichl · Misti Allison · Renee Feldman · Michelle Gandolf · Benjamin Walter · Brittany Lapin
29 October 2025 at 20:15
Background: Patient recruitment remains a critical challenge in clinical research. Although the integration of electronically collected patient-entered data within clinical practices enables innovative recruitment approaches, existing methods present challenges such as increased patient burden and potential violation of autonomy. A more nuanced approach involves identifying patient attributes associated with higher propensity for research participation, enabling research teams to efficiently prioritize outreach efforts. Objective: This study aims to (1) develop patient-reported questions reflecting perceptions about research participation and (2) determine whether patient responses are predictive of interest in joining a precision medicine registry. Methods: This mixed methods study used an exploratory sequential design in 2 phases. Phase 1 involved cognitive interviews with 32 patients recruited through the Cleveland Clinic Healthcare Partners program to develop “research perception” questions. Participants evaluated 9 candidate questions that were based on a literature review of research participation factors. Three questions were selected for implementation. Phase 2 was a cross-sectional cohort study incorporating these 3 questions into routine electronic questionnaires completed by primary care patients through the patient portal. The study population included 1077 patients who completed both “research perception” and “research recruitment” questions between August 2018 and April 2019. Diagnostic accuracy was assessed using receiver operating characteristic curve analysis, and multivariable logistic regression models evaluated associations while adjusting for demographic and health factors. Results: Phase 1 revealed strong research support among participants, with 97% (31/32) agreeing that research should be part of the institution’s mission and 100% (32/32) affirming that research enhances patient care. Phase 2 included 1077 patients (mean age 48.3, SD 16.3 years; 625/1065 female, 58.68%; 661/1005 White, 65.77%), of whom 278 (25.8%) expressed interest in being contacted about the precision medicine registry. Patients expressing interest were older and had worse self-reported health, more depressive symptoms, and greater social needs. “Strongly agree” and “very important” responses to any “research perception” question were significantly associated with study interest, with adjusted odds ratios ranging from 6.36 (95% CI 2.77-14.6) to 17.6 (95% CI 5.08-61.1; P<.001). The “research perception” questions demonstrated high sensitivity (>80%) but limited specificity (24%-31%). Conclusions: Patient-reported questions assessing research participation likelihood can help identify patients more likely to enroll in clinical studies. This approach enables effective recruitment prioritization while preserving patient autonomy and reducing patient burden. High sensitivity makes these questions valuable as screening tools, although limited specificity suggests use for prioritizing rather than excluding participants. Further validation across different trial types and populations is warranted.
Received — 29 October 2025 ⏭ Favourites

Test-Time Tuned Language Models Enable End-to-end De Novo Molecular Structure Generation from MS/MS Spectra

✇cs.AI, q-bio.NC updates on arXiv.org
By: Laura Mismetti · Marvin Alberts · Andreas Krause · Mara Graziani
29 October 2025 at 12:00
arXiv:2510.23746v1 Announce Type: new Abstract: Tandem Mass Spectrometry enables the identification of unknown compounds in crucial fields such as metabolomics, natural product discovery and environmental analysis. However, current methods rely on database matching from previously observed molecules, or on multi-step pipelines that require intermediate fragment or fingerprint prediction. This makes finding the correct molecule highly challenging, particularly for compounds absent from reference databases. We introduce a framework that, by leveraging test-time tuning, enhances the learning of a pre-trained transformer model to address this gap, enabling end-to-end de novo molecular structure generation directly from the tandem mass spectra and molecular formulae, bypassing manual annotations and intermediate steps. We surpass the de-facto state-of-the-art approach DiffMS on two popular benchmarks NPLIB1 and MassSpecGym by 100% and 20%, respectively. Test-time tuning on experimental spectra allows the model to dynamically adapt to novel spectra, and the relative performance gain over conventional fine-tuning is of 62% on MassSpecGym. When predictions deviate from the ground truth, the generated molecular candidates remain structurally accurate, providing valuable guidance for human interpretation and more reliable identification.

Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gang Chen · Changshuo Liu · Gene Anne Ooi · Marcus Tan · Zhongle Xie · Jianwei Yin · James Wei Luen Yip · Wenqiao Zhang · Jiaqi Zhu · Beng Chin Ooi
29 October 2025 at 12:00
arXiv:2510.24551v1 Announce Type: new Abstract: Generative Artificial Intelligence (GenAI) is taking the world by storm. It promises transformative opportunities for advancing and disrupting existing practices, including healthcare. From large language models (LLMs) for clinical note synthesis and conversational assistance to multimodal systems that integrate medical imaging, electronic health records, and genomic data for decision support, GenAI is transforming the practice of medicine and the delivery of healthcare, such as diagnosis and personalized treatments, with great potential in reducing the cognitive burden on clinicians, thereby improving overall healthcare delivery. However, GenAI deployment in healthcare requires an in-depth understanding of healthcare tasks and what can and cannot be achieved. In this paper, we propose a data-centric paradigm in the design and deployment of GenAI systems for healthcare. Specifically, we reposition the data life cycle by making the medical data ecosystem as the foundational substrate for generative healthcare systems. This ecosystem is designed to sustainably support the integration, representation, and retrieval of diverse medical data and knowledge. With effective and efficient data processing pipelines, such as semantic vector search and contextual querying, it enables GenAI-powered operations for upstream model components and downstream clinical applications. Ultimately, it not only supplies foundation models with high-quality, multimodal data for large-scale pretraining and domain-specific fine-tuning, but also serves as a knowledge retrieval backend to support task-specific inference via the agentic layer. The ecosystem enables the deployment of GenAI for high-quality and effective healthcare delivery.

Integrating Genomics into Multimodal EHR Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jonathan Amar · Edward Liu · Alessandra Breschi · Liangliang Zhang · Pouya Kheradpour · Sylvia Li · Lisa Soleymani Lehmann · Alessandro Giulianelli · Matt Edwards · Yugang Jia · David Nola · Raghav Mani · Pankaj Vats · Jesse Tetreault · T. J. Chen · Cory Y. McLean
29 October 2025 at 12:00
arXiv:2510.23639v1 Announce Type: cross Abstract: This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.

Closing Gaps: An Imputation Analysis of ICU Vital Signs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alisher Turubayev · Anna Shopova · Fabian Lange · Mahmut Kamalak · Paul Mattes · Victoria Ayvasky · Bert Arnrich · Bjarne Pfitzner · Robin P. van de Water
29 October 2025 at 12:00
arXiv:2510.24217v1 Announce Type: cross Abstract: As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the data that could negatively impact prediction performance. Previous works have introduced numerous time-series imputation techniques. Nevertheless, more comprehensive work is needed to compare a representative set of methods for imputing ICU vital signs and determine the best practice. In reality, ad-hoc imputation techniques that could decrease prediction accuracy, like zero imputation, are still used. In this work, we compare established imputation techniques to guide researchers in improving the performance of clinical prediction models by selecting the most accurate imputation technique. We introduce an extensible and reusable benchmark with currently 15 imputation and 4 amputation methods, created for benchmarking on major ICU datasets. We hope to provide a comparative basis and facilitate further ML development to bring more models into clinical practice.

Dynamic Monitoring of Recurrent Ovarian Cancer Using Serial ctDNA: A Real-World Case Series

✇MRD
By: Eric Rios-Doria · Jonathan B Reichel · Marc R Radke · Enna Manhardt · Mayumi Rubin-Saika · Christina Lockwood · Elizabeth M Swisher · Kalyan Banda
28 October 2025 at 18:00

Curr Oncol. 2025 Oct 21;32(10):585. doi: 10.3390/curroncol32100585.

ABSTRACT

Recurrent ovarian cancer (OC) is challenging to detect early using current methods like CA-125 and imaging. Circulating tumor DNA (ctDNA) may improve disease monitoring. Here, we assess the real-world clinical utility of serial ctDNA analyses in patients with recurrent OC. We analyzed serial plasma samples (N = 23) from six patients with recurrent OC using a tumor-informed next-generation sequencing assay targeting 68 cancer-related genes developed at the University of Washington. ctDNA variant allele frequencies (VAFs) were correlated with CA-125 levels, radiographic findings, and clinical outcomes. ctDNA levels generally reflected clinical status, accurately mirroring disease progression and therapeutic response. In one patient, rising ctDNA preceded clinical recurrence by four months, despite normal CA-125 and imaging, highlighting its potential advantage. Conversely, some patients exhibited clinical progression with undetectable ctDNA, indicating limitations in assay sensitivity, biological factors, or metastatic sites (e.g., brain metastases). ctDNA and CA-125 showed complementary value in most cases, suggesting potential combined use in clinical monitoring. Our findings demonstrate that ctDNA is a promising biomarker to complement existing monitoring approaches for recurrent OC. In some cases, capable of predicting relapse and treatment response ahead of current clinical indicators. However, identified discordances underscore technical and biological challenges that warrant further investigation. Larger prospective studies are necessary to refine ctDNA's clinical utility and integration into personalized OC care.

PMID:41149505 | PMC:PMC12563156 | DOI:10.3390/curroncol32100585

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