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Received — 26 November 2025 ⏭ Favourites

Toward explainable AI approaches for breast imaging: adapting foundation models to diverse populations

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guilherme J. Cavalcante · Jos\'e Gabriel A. Moreira · Gabriel A. B. do Nascimento · Vincent Dong · Alex Nguyen · Tha\'is G. do R\^ego · Yuri Malheiros · Telmo M. Silva Filho · Carla R. Zeballos Torrez · James C. Gee · Anne Marie McCarthy · Andrew D. A. Maidment · Bruno Barufaldi
25 November 2025 at 13:00
arXiv:2511.17828v1 Announce Type: cross Abstract: Foundation models hold promise for specialized medical imaging tasks, though their effectiveness in breast imaging remains underexplored. This study leverages BiomedCLIP as a foundation model to address challenges in model generalization. BiomedCLIP was adapted for automated BI-RADS breast density classification using multi-modality mammographic data (synthesized 2D images, digital mammography, and digital breast tomosynthesis). Using 96,995 images, we compared single-modality (s2D only) and multi-modality training approaches, addressing class imbalance through weighted contrastive learning. Both approaches achieved similar accuracy (multi-modality: 0.74, single-modality: 0.73), with the multi-modality model offering broader applicability across different imaging modalities and higher AUC values consistently above 0.84 across BI-RADS categories. External validation on the RSNA and EMBED datasets showed strong generalization capabilities (AUC range: 0.80-0.93). GradCAM visualizations confirmed consistent and clinically relevant attention patterns, highlighting the models interpretability and robustness. This research underscores the potential of foundation models for breast imaging applications, paving the way for future extensions for diagnostic tasks.

Towards Automating Data Access Permissions in AI Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuhao Wu · Ke Yang · Franziska Roesner · Tadayoshi Kohno · Ning Zhang · Umar Iqbal
25 November 2025 at 13:00
arXiv:2511.17959v1 Announce Type: cross Abstract: As AI agents attempt to autonomously act on users' behalf, they raise transparency and control issues. We argue that permission-based access control is indispensable in providing meaningful control to the users, but conventional permission models are inadequate for the automated agentic execution paradigm. We therefore propose automated permission management for AI agents. Our key idea is to conduct a user study to identify the factors influencing users' permission decisions and to encode these factors into an ML-based permission management assistant capable of predicting users' future decisions. We find that participants' permission decisions are influenced by communication context but importantly individual preferences tend to remain consistent within contexts, and align with those of other participants. Leveraging these insights, we develop a permission prediction model achieving 85.1% accuracy overall and 94.4% for high-confidence predictions. We find that even without using permission history, our model achieves an accuracy of 66.9%, and a slight increase of training samples (i.e., 1-4) can substantially increase the accuracy by 10.8%.

Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizons

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fermat Leukam · Rock Stephane Koffi · Prudence Djagba
25 November 2025 at 13:00
arXiv:2511.18076v1 Announce Type: cross Abstract: This research proposes an enhancement to the innovative portfolio optimization approach using the G-Learning algorithm, combined with parametric optimization via the GIRL algorithm (G-learning approach to the setting of Inverse Reinforcement Learning) as presented by. The goal is to maximize portfolio value by a target date while minimizing the investor's periodic contributions. Our model operates in a highly volatile market with a well-diversified portfolio, ensuring a low-risk level for the investor, and leverages reinforcement learning to dynamically adjust portfolio positions over time. Results show that we improved the Sharpe Ratio from 0.42, as suggested by recent studies using the same approach, to a value of 0.483 a notable achievement in highly volatile markets with diversified portfolios. The comparison between G-Learning and GIRL reveals that while GIRL optimizes the reward function parameters (e.g., lambda = 0.0012 compared to 0.002), its impact on portfolio performance remains marginal. This suggests that reinforcement learning methods, like G-Learning, already enable robust optimization. This research contributes to the growing development of reinforcement learning applications in financial decision-making, demonstrating that probabilistic learning algorithms can effectively align portfolio management strategies with investor needs.

The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement from Clinical Decision-making Quality

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dou Liu · Ying Long · Sophia Zuoqiu · Kaipeng Xie · Runze Yang · Di Liu · Kang Li · Yiting Lin · Hanyi Liu · Rong Yin · Tian Tang
25 November 2025 at 13:00
arXiv:2511.18084v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly adopted in clinical decision support, yet aligning them with the multifaceted reasoning pathways of real-world medicine remains a major challenge. Using more than 8,000 infertility treatment records, we systematically evaluate four alignment strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and In-Context Learning (ICL) through a dual-layer framework combining automatic benchmarks with blinded doctor-in-the-loop assessments. GRPO achieves the highest algorithmic accuracy across multiple decision layers, confirming the value of reinforcement-based optimization for structured prediction tasks. However, clinicians consistently prefer the SFT model, citing clearer reasoning processes (p = 0.035) and higher therapeutic feasibility (p = 0.019). In blinded pairwise comparisons, SFT attains the highest winning rate (51.2%), outperforming both GRPO (26.2%) and even physicians' original decisions (22.7%). These results reveal an alignment paradox: algorithmic improvements do not necessarily translate into higher clinical trust, and may diverge from human-centered preferences. Our findings highlight the need for alignment strategies that prioritize clinically interpretable and practically feasible reasoning, rather than solely optimizing decision-level accuracy.

Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chibuike E. Ugwu · Roschelle Fritz · Diane J. Cook · Janardhan Rao Doppa
25 November 2025 at 13:00
arXiv:2511.18334v1 Announce Type: cross Abstract: Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don't know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.

Are Large Vision Language Models Truly Grounded in Medical Images? Evidence from Italian Clinical Visual Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Federico Felizzi · Olivia Riccomi · Michele Ferramola · Francesco Andrea Causio · Manuel Del Medico · Vittorio De Vita · Lorenzo De Mori · Alessandra Piscitelli Pietro Eric Risuleo · Bianca Destro Castaniti · Antonio Cristiano Alessia Longo · Luigi De Angelis · Mariapia Vassalli · Marcello Di Pumpo
25 November 2025 at 13:00
arXiv:2511.19220v1 Announce Type: cross Abstract: Large vision language models (VLMs) have achieved impressive performance on medical visual question answering benchmarks, yet their reliance on visual information remains unclear. We investigate whether frontier VLMs demonstrate genuine visual grounding when answering Italian medical questions by testing four state-of-the-art models: Claude Sonnet 4.5, GPT-4o, GPT-5-mini, and Gemini 2.0 flash exp. Using 60 questions from the EuropeMedQA Italian dataset that explicitly require image interpretation, we substitute correct medical images with blank placeholders to test whether models truly integrate visual and textual information. Our results reveal striking variability in visual dependency: GPT-4o shows the strongest visual grounding with a 27.9pp accuracy drop (83.2% [74.6%, 91.7%] to 55.3% [44.1%, 66.6%]), while GPT-5-mini, Gemini, and Claude maintain high accuracy with modest drops of 8.5pp, 2.4pp, and 5.6pp respectively. Analysis of model-generated reasoning reveals confident explanations for fabricated visual interpretations across all models, suggesting varying degrees of reliance on textual shortcuts versus genuine visual analysis. These findings highlight critical differences in model robustness and the need for rigorous evaluation before clinical deployment.

AI-Assisted Cardiovascular Risk Assessment by General Practitioners in Resource-Constrained Indonesian Settings Using a Conceptual Prototype: Randomized Controlled Study

✇Journal of Medical Internet Research
By: Anindya Pradipta Susanto · David Lyell · Bambang Widyantoro · Dafsah Arifa Juzar · Anwar Santoso · Shlomo Berkovsky · Farah Magrabi
26 November 2025 at 05:30
Background: Preventive strategies integrated with digital health and artificial intelligence (AI), have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these advances, their capacity to enhance clinical decision-making in resource-constrained settings remains largely unexplored. Objective: We conducted a randomised controlled study to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management in primary prevention. Methods: In a three-way within-subject randomised design, doctors completed nine clinical vignettes representative of primary care presentations in a resource-constrained outpatient setting. For each vignette, participants assessed 10-year ASCVD risk and made management decisions using either a conceptual prototype of AI-based CDS, automated CDS, or no decision support. The conceptual prototype represented contemporary risk calculators based on traditional machine learning models (e.g., random forest, neural networks, logistic regression) that incorporate additional predictors alongside traditional risk factors. Primary outcomes were correct risk assessment and patient management (prescription of aspirin, statins, and anti-hypertensives; referral for advanced examinations). Decision-making time and perceptions about AI utility were also measured. Results: 102 doctors from all seven geographical regions of Indonesia participated. Most participants were 26–35 years (83%), 56% male, with a median of six years of clinical experience (IQR=4.75). AI-based CDS improved risk assessment by 27% (2(2, n=102) = 48.875, P<.001 compared to unassisted or one additional correct risk classification for every patients where doctors use ai needed treat nnt="3.7;" ci the prescription of statins also improved by n="102)=" p in pairwise comparisons assisted with ai-based cds correctly assessed significantly more cases adjusted and prescribed appropriate statin often medium effect size r=".35)" control. ai-assisted required less time marginal means sec vs f however improvements aspirin anti-hypertensives did not reach statistical significance. no improvement was observed referral decisions. participants generally viewed positively agreeing strongly that they would follow its recommendations indicating it if given access. believed could enhance efficiency assessment particularly high-volume primary care settings while noting need verify against clinical guidelines each patient. conclusions: coupled reduced decision-making highlight potential utility ascvd resource-constrained efficient healthcare resources is crucial. further research ascertain whether this online study translate real-world low-resource settings.>
Received — 25 November 2025 ⏭ Favourites

ConCISE: A Reference-Free Conciseness Evaluation Metric for LLM-Generated Answers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Seyed Mohssen Ghafari · Ronny Kol · Juan C. Quiroz · Nella Luan · Monika Patial · Chanaka Rupasinghe · Herman Wandabwa · Luiz Pizzato
24 November 2025 at 13:00
arXiv:2511.16846v1 Announce Type: cross Abstract: Large language models (LLMs) frequently generate responses that are lengthy and verbose, filled with redundant or unnecessary details. This diminishes clarity and user satisfaction, and it increases costs for model developers, especially with well-known proprietary models that charge based on the number of output tokens. In this paper, we introduce a novel reference-free metric for evaluating the conciseness of responses generated by LLMs. Our method quantifies non-essential content without relying on gold standard references and calculates the average of three calculations: i) a compression ratio between the original response and an LLM abstractive summary; ii) a compression ratio between the original response and an LLM extractive summary; and iii) wordremoval compression, where an LLM removes as many non-essential words as possible from the response while preserving its meaning, with the number of tokens removed indicating the conciseness score. Experimental results demonstrate that our proposed metric identifies redundancy in LLM outputs, offering a practical tool for automated evaluation of response brevity in conversational AI systems without the need for ground truth human annotations.

Artificial Intelligence Index Report 2025

✇cs.AI, q-bio.NC updates on arXiv.org
By: Nestor Maslej · Loredana Fattorini · Raymond Perrault · Yolanda Gil · Vanessa Parli · Njenga Kariuki · Emily Capstick · Anka Reuel · Erik Brynjolfsson · John Etchemendy · Katrina Ligett · Terah Lyons · James Manyika · Juan Carlos Niebles · Yoav Shoham · Russell Wald · Toby Walsh · Armin Hamrah · Lapo Santarlasci · Julia Betts Lotufo · Alexandra Rome · Andrew Shi · Sukrut Oak
24 November 2025 at 13:00
arXiv:2504.07139v3 Announce Type: replace Abstract: Welcome to the eighth edition of the AI Index report. The 2025 Index is our most comprehensive to date and arrives at an important moment, as AI's influence across society, the economy, and global governance continues to intensify. New in this year's report are in-depth analyses of the evolving landscape of AI hardware, novel estimates of inference costs, and new analyses of AI publication and patenting trends. We also introduce fresh data on corporate adoption of responsible AI practices, along with expanded coverage of AI's growing role in science and medicine. Since its founding in 2017 as an offshoot of the One Hundred Year Study of Artificial Intelligence, the AI Index has been committed to equipping policymakers, journalists, executives, researchers, and the public with accurate, rigorously validated, and globally sourced data. Our mission has always been to help these stakeholders make better-informed decisions about the development and deployment of AI. In a world where AI is discussed everywhere - from boardrooms to kitchen tables - this mission has never been more essential. The AI Index continues to lead in tracking and interpreting the most critical trends shaping the field - from the shifting geopolitical landscape and the rapid evolution of underlying technologies, to AI's expanding role in business, policymaking, and public life. Longitudinal tracking remains at the heart of our mission. In a domain advancing at breakneck speed, the Index provides essential context - helping us understand where AI stands today, how it got here, and where it may be headed next. Recognized globally as one of the most authoritative resources on artificial intelligence, the AI Index has been cited in major media outlets such as The New York Times, Bloomberg, and The Guardian; referenced in hundreds of academic papers; and used by policymakers and government agencies around the world.

Multimodal analysis of whole slide images in colorectal cancer

✇npj Digital Medicine
By: Jitendra Jonnagaddala · Miljana Shulajkovska · Anton Gradišek · Toni Rose Jue · Qifeng Zhou · Yuzhi Guo · Jamil Mahmoud El Chayeb · Ruijiang Li · Jana Lipkova · Jakob Nikolas Kather · Junzhou Huang
24 November 2025 at 08:00

npj Digital Medicine, Published online: 24 November 2025; doi:10.1038/s41746-025-02095-y

Multimodal analysis of whole slide images in colorectal cancer

Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas

✇(Multiomics OR Omics) AND (Pancreatic)
By: Kathleen Noller · Jiaying Lai · Daniel Lesperance · Ricky S Adkins · Ahmed Elhossiny · Paola A Guerrero · Kimal I Rajapakshe · Anirban Maitra · Michelle Giglio · Anup Mahurkar · Owen White · Marina Pasca Di Magliano · Michael F Ochs · Luciane T Kagohara · Laura D Wood · Rachel Karchin · Elana J Fertig
24 November 2025 at 19:00

bioRxiv [Preprint]. 2025 Nov 4:2025.11.03.686234. doi: 10.1101/2025.11.03.686234.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

PMID:41279473 | PMC:PMC12637499 | DOI:10.1101/2025.11.03.686234

Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection

✇MRD
By: Antonio De Falco · Piera Grisolia · Raffaella Giuffrida · Clara Iannarone · Cinzia Graziano · Marianna Scrima · Alessia Maria Cossu · Rossella Tufano · Maria Vanessa Yow · Chloe Marissa Brown · Palak Bajaj · Marco Bocchetti · Pier Vitale Nuzzo · Floriana Morgillo · Francesco Caraglia · Maria Carminia Delle Corte · Gaetano Di Guida · Teresa Troiani · Fortunato Ciardiello · Maria Rosaria Rizzo · Alfonso Fiorelli · Noemi Maria Giorgiano · Davide Arcaniolo · Giampiero Della Rosa · Marco De Sio · Alessia Covre · Anna Maria Di Giacomo · Luana Calabrò · Paolo Fontana · Marzia Mare · Ola Landgren · Damian J Green · Alex Lesokhin · David G Coffey · Nipun Merchant · Jashodeep Datta · Stefano Forte · Michele Maio · Michele Caraglia · Michele Ceccarelli
24 November 2025 at 19:00

bioRxiv [Preprint]. 2025 Oct 21:2025.10.20.683167. doi: 10.1101/2025.10.20.683167.

ABSTRACT

Liquid biopsy offers a minimally invasive opportunity to detect and monitor cancers through analysis of cell-free DNA (cfDNA). However, current approaches face challenges of limited sensitivity at low tumor fractions, technical variability, and poor generalization across cohorts. Tumor-informed targeted methods offer high specificity but suffer from low sensitivity due to random sampling, tumor evolution and adaptation (including resistance mechanisms), and other sources of heterogeneity. Conversely, tumor-naive genome-wide methods can increase sensitivity but often sacrifice specificity, particularly at low tumor fractions. We developed Fragmentomics Analysis for Tumor Evaluation with AI (Fate-AI), a multimodal framework that integrates fragmentomic and methylation-derived features from low-pass whole-genome sequencing (LPWGS) and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). It employs a knowledge-informed strategy to select recurrently altered genomic regions and tissue-specific methylation loci to combine the advantages of tumor-naive approaches with the specificity of tumor-informed approaches. This approach derives robust per-sample normalized features that mitigate batch effects and enhance cross-cohort reproducibility. We evaluated Fate-AI on a total of 1,219 plasma samples spanning ten cancer types and healthy controls from multiple laboratories and sequencing centers, including 432 newly profiled cases (280 with both cfMeDIP-seq and LPWGS) together with 787 samples from four independent public datasets. Fate-AI achieved superior sensitivity and specificity compared to state-of-the-art methods, detecting tumor-derived signals at fractions as low as 10-5 in experimental dilutions. Fate-AI scores correlated with disease stage and tracked longitudinal progression, anticipating relapse months before clinical progression. Furthermore, Fate-AI enabled tissue-of-origin classification, with AUCs ranging from 0.84 to 0.97 across six cancer types. Collectively, our results demonstrate that Fate-AI provides a sensitive, generalizable, and clinically actionable platform for early detection, minimal residual disease monitoring, and tissue-of-origin classification, supporting its potential as a liquid biopsy framework in precision oncology.

PMID:41278930 | PMC:PMC12633305 | DOI:10.1101/2025.10.20.683167

Received — 24 November 2025 ⏭ Favourites

Impact of Digital Interventions on the Treatment Burden of Patients With Chronic Conditions: Systematic Review

✇Journal of Medical Internet Research
By: Manria Polus · Pantea Keikhosrokiani · Olli Korhonen · Woubshet Behutiye · Minna Isomursu
22 November 2025 at 02:00
Background: Digital interventions can provide cost-effective, quality health care for patients with chronic conditions. Patients with chronic conditions often are burdened by a substantial load of adhering to a treatment regimen and suffer from impacts on their function and well-being. This treatment burden has consequences for treatment adherence and disease outcomes. Digital interventions have the potential to alleviate the burden, but they also may cause new challenges and an increased workload for the patient. Previous reviews have examined digital interventions or treatment burden separately, but there is a lack of systematic reviews on the intersection of digital interventions, treatment burden, and chronic conditions. Objective: This systematic review aimed to evaluate the evidence of how digital interventions impact the treatment burden experienced by people with chronic conditions, and to assess the quality of this evidence. Methods: We searched databases PubMed, Scopus, Web of Science, ACM, PubMed Central, and CINAHL for articles published between January 1, 2013, and June 17, 2025. We included studies that had key topics related to chronic conditions, treatment burden, and digital interventions. A total of 2 reviewers independently screened the articles in 2 stages, extracted data on study design, participant characteristics, intervention type, and treatment burden outcomes from included articles, and assessed their quality using the Critical Appraisal tools from the Joanna Briggs Institute. A convergent integrated approach was used for data synthesis and integration, where quantitative data were converted into qualitative data, and the qualitative and quantitative evidence were analyzed and categorized together. Results: We included 46 relevant studies in total. We categorized the interventions into 4 types: Telehealth, informational resources, self-management tools, and facilitated tools. The results of this study indicate that digital interventions mostly support patients with chronic conditions with their treatment burden, with minor concerns of increasing treatment burden. The main benefits are support with self-management, informational support, and easier ways to contact health care professionals. The main concerns were accessibility issues, time-consuming tools, and causing fear and anxiety. Conclusions: Our findings demonstrate how treatment burden is a relevant concept for future digital health care research and practice. Digital interventions can help patients with their treatment burden by supporting self-management, improving access to health care, improving patients’ experience, and addressing relevant concerns. More research is needed about conditions with low or medium initial treatment burden.

Organoid-based precision cancer modeling: New frontier in lung cancer research

✇Omics In Lung
By: Hideki Terai · Hiroyuki Yasuda · Toshiro Sato
22 November 2025 at 19:00

Cell Rep. 2025 Nov 20;44(12):116595. doi: 10.1016/j.celrep.2025.116595. Online ahead of print.

ABSTRACT

Lung cancer remains a leading cause of cancer-related mortality globally, underscoring the need for advanced preclinical models that accurately recapitulate disease biology. Recent advances in organoid technology have enabled the establishment of patient-derived lung cancer organoids (LCOs), which faithfully reproduce the histological, genetic, and phenotypic features of primary tumors. This organoid-based precision modeling facilitates deeper insights into tumor biology and disease progression, supporting the identification of novel therapeutic targets and biomarkers. In this review, we summarize recent progress in LCO-based precision modeling, focusing on their ability to preserve tumor heterogeneity, link genotype and phenotype through multi-omics integration, and explore tumor-microenvironment interactions via gene editing and co-culture systems. We also highlight the growing importance of LCO biobanks and international collaborations in translational research. Despite challenges such as low establishment efficiency, LCO-based precision modeling offers a powerful platform for understanding lung cancer pathogenesis and guiding the development of more effective therapies.

PMID:41273722 | DOI:10.1016/j.celrep.2025.116595

  • ✇InfoQ
  • Olmo 3 Release Provides Full Transparency into Model Development and Training Robert Krzaczyński
    The Allen Institute for AI has unveiled Olmo 3, an open-source language model family that empowers developers with full access to the model lifecycle, from training datasets to checkpoints. Featuring reasoning-focused variants and robust tools for post-training modifications, Olmo 3 promotes transparency, experimentation, and community collaboration, driving innovations in AI. By Robert Krzaczyński
     
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Olmo 3 Release Provides Full Transparency into Model Development and Training

✇InfoQ
By: Robert Krzaczyński
23 November 2025 at 00:00

The Allen Institute for AI has unveiled Olmo 3, an open-source language model family that empowers developers with full access to the model lifecycle, from training datasets to checkpoints. Featuring reasoning-focused variants and robust tools for post-training modifications, Olmo 3 promotes transparency, experimentation, and community collaboration, driving innovations in AI.

By Robert Krzaczyński
Received — 21 November 2025 ⏭ Favourites

Pan-cancer prevalence, risk, and clinical and demographic characteristics of Lynch Syndrome-associated variants in BioBank Japan

✇(Multiomics OR Omics) AND (Pancreatic)
By: Keijiro Mizukami · Yoshiaki Usui · Yusuke Iwasaki · Kouya Shiraishi · Makoto Hirata · Yoichiro Kamatani · Mikiko Endo · Satoshi Takahashi · Yoshiki Mochizuki · Mitusyo Yamaguchi · Takashi Kohno · Koichi Matsuda · Kokichi Sugano · Teruhiko Yoshida · Hidewaki Nakagawa · Chikashi Terao · Yuriko N Koyanagi · Keitaro Matsuo · Yoshinori Murakami · Amanda B Spurdle · Yukihide Momozawa
19 November 2025 at 19:00

Commun Med (Lond). 2025 Nov 13. doi: 10.1038/s43856-025-01231-9. Online ahead of print.

ABSTRACT

BACKGROUND: Although germline testing for DNA mismatch repair (MMR) genes is routinely performed, clinical guidelines highlight evidence gaps due to limited populations and biases. We examined germline pathogenic variants of MMR genes (MLH1, MSH2, MSH6, and PMS2) in 112,927 unselected individuals from BioBank Japan.

METHODS: We analyzed 74,085 cancer patients with 23 cancer types and 38,842 controls matched by sex, age, and hospital area from BioBank Japan, collected between April 2003 and March 2018. Germline pathogenic variants in the coding regions and 2 bp flanking intronic sequences of MMR genes were identified using a multiplex PCR-based target sequencing method. We examined associations with cancer types and demographic characterization of the pathogenic variants, comparing findings to existing clinical guidelines.

RESULTS: Here we show 228 pathogenic variants identified in MMR genes, with pathogenic MSH6 variants most frequently observed in endometrial cancer and 12 other significant associations. Twelve other significant associations are noted across a broad range of odds ratios, whereas pancreatic cancer exhibits no such association. Pathogenic variant carriers are diagnosed up to 12.4 years earlier than non-carriers, and colorectal and gastric cancers are diagnosed up to 16.4 years later than indicated by the guidelines. Higher carrier frequencies are observed in patients with both colorectal and endometrial cancers (24.8%) and in those with endometrial cancer and a family history of endometrial (26.0%) or colorectal (16.1%) cancers.

CONCLUSIONS: This study provides critical insights for clinical guidelines on the associations between cancer types, age at diagnosis, and carrier frequency.

PMID:41258140 | DOI:10.1038/s43856-025-01231-9

Latent plasticity of the human pancreas across development, health, and disease

✇(Multiomics OR Omics) AND (Pancreatic)
By: Elisabetta Mereu · Diego Balboa · Johannes Liebig · Aitor Gonzalez-Herrero · Anna Martinez-Casals · Mariya Mardamshina · Fanny Mollandin · Felix Schicktanz · Luca Tosti · Valerie Vandenbempt · Dana Avrahami · Edgar Bernardo · Frida Björklund · Robert Lorenz Chua · Marten Engelse · Javier García-Hurtado · Nathalie Groen · Maaike Hanegraaf · Pablo Iañez · Katharina Jechow · Björn Konukiewitz · Christian Lawerenz · Domenica Marchese · Mauro J Muraro · Silvia Pellegrini · Valeria Sordi · Alexander Sudy · Ulrike Taron · Foo Wei Ten · Timo Trefzer · Sven Twardziok · Maarten van Agen · Françoise Carlotti · Eelco de Koning · Jorge Ferrer · Benjamin Glaser · Holger Heyn · Emma Lundberg · Lorenzo Piemonti · Katja Steiger · Alexander van Oudenaarden · Wilko Weichert · Christian Conrad · Roland Eils
19 November 2025 at 19:00

bioRxiv [Preprint]. 2025 Oct 3:2025.10.01.679230. doi: 10.1101/2025.10.01.679230.

ABSTRACT

The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating sc/snRNA-seq, snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and uncover HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.

PMID:41256699 | PMC:PMC12622017 | DOI:10.1101/2025.10.01.679230

Received — 19 November 2025 ⏭ Favourites

Foundation Models in Medical Imaging: A Review and Outlook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Vivien van Veldhuizen · Vanessa Botha · Chunyao Lu · Melis Erdal Cesur · Kevin Groot Lipman · Edwin D. de Jong · Hugo Horlings · Cl\'arisa I. Sanchez · Cees G. M. Snoek · Lodewyk Wessels · Ritse Mann · Eric Marcus · Jonas Teuwen
19 November 2025 at 13:00
arXiv:2506.09095v4 Announce Type: replace-cross Abstract: Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.

Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF)

✇cs.AI, q-bio.NC updates on arXiv.org
By: Robert Gigiu
19 November 2025 at 13:00
arXiv:2511.11590v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly embedded in NHS workflows, but its probabilistic and adaptive behaviour conflicts with the deterministic assumptions underpinning existing clinical-safety standards. DCB0129 and DCB0160 provide strong governance for conventional software yet do not define how AI-specific transparency, interpretability, or model drift should be evidenced within Safety Cases, Hazard Logs, or post-market monitoring. This paper proposes an Explainability-Enabled Clinical Safety Framework (ECSF) that integrates explainability into the DCB0129/0160 lifecycle, enabling Clinical Safety Officers to use interpretability outputs as structured safety evidence without altering compliance pathways. A cross-regulatory synthesis mapped DCB clauses to principles from Good Machine Learning Practice, the NHS AI Assurance and T.E.S.T. frameworks, and the EU AI Act. The resulting matrix links regulatory clauses, principles, ECSF checkpoints, and suitable explainability outputs. ECSF introduces five checkpoints: global transparency for hazard identification, case-level interpretability for verification, clinician usability for evaluation, traceable decision pathways for risk control, and longitudinal interpretability monitoring for post-market surveillance. Techniques such as SHAP, LIME, Integrated Gradients, saliency mapping, and attention visualisation are mapped to corresponding DCB artefacts. ECSF reframes explainability as a core element of clinical-safety assurance, bridging deterministic risk governance with the probabilistic behaviour of AI and supporting alignment with GMLP, the EU AI Act, and NHS AI Assurance principles.
Received — 18 November 2025 ⏭ Favourites

CLINB: A Climate Intelligence Benchmark for Foundational Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Michelle Chen Huebscher · Katharine Mach · Aleksandar Stani\'c · Markus Leippold · Ben Gaiarin · Zeke Hausfather · Elisa Rawat · Erich Fischer · Massimiliano Ciaramita · Joeri Rogelj · Christian Buck · Lierni Sestorain Saralegui · Reto Knutti
18 November 2025 at 13:00
arXiv:2511.11597v1 Announce Type: new Abstract: Evaluating how Large Language Models (LLMs) handle complex, specialized knowledge remains a critical challenge. We address this through the lens of climate change by introducing CLINB, a benchmark that assesses models on open-ended, grounded, multimodal question answering tasks with clear requirements for knowledge quality and evidential support. CLINB relies on a dataset of real users' questions and evaluation rubrics curated by leading climate scientists. We implement and validate a model-based evaluation process and evaluate several frontier models. Our findings reveal a critical dichotomy. Frontier models demonstrate remarkable knowledge synthesis capabilities, often exhibiting PhD-level understanding and presentation quality. They outperform "hybrid" answers curated by domain experts assisted by weaker models. However, this performance is countered by failures in grounding. The quality of evidence varies, with substantial hallucination rates for references and images. We argue that bridging this gap between knowledge synthesis and verifiable attribution is essential for the deployment of AI in scientific workflows and that reliable, interpretable benchmarks like CLINB are needed to progress towards building trustworthy AI systems.
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