Nature Medicine, Published online: 10 November 2025; doi:10.1038/s41591-025-04065-z

Author Correction: Global burden of chikungunya virus infections and the potential benefit of vaccination campaigns

Nature Biotechnology, Published online: 07 November 2025; doi:10.1038/s41587-025-02941-0

Publisher Correction: Deep-learning-based virtual screening of antibacterial compounds

Nature Medicine, Published online: 05 November 2025; doi:10.1038/s41591-025-03982-3

Pretrained using 335,645 whole-slide images, a foundation model is developed to provide representations for slide- and patient-level tasks. It is capable of performing clinical tasks and generating reports even in data-scarce scenarios, such as rare cancer diagnosis and survival prediction, without requiring further fine-tuning.

arXiv:2511.00094v1 Announce Type: cross Abstract: Robotic systems have become integral to smart environments, enabling applications ranging from urban surveillance and automated agriculture to industrial automation. However, their effective operation in dynamic settings - such as smart cities and precision farming - is challenged by continuously evolving topographies and environmental conditions. Traditional control systems often struggle to adapt quickly, leading to inefficiencies or operational failures. To address this limitation, we propose a novel framework for autonomous and dynamic reconfiguration of robotic controllers using Digital Twin technology. Our approach leverages a virtual replica of the robot's operational environment to simulate and optimize movement trajectories in response to real-world changes. By recalculating paths and control parameters in the Digital Twin and deploying the updated code to the physical robot, our method ensures rapid and reliable adaptation without manual intervention. This work advances the integration of Digital Twins in robotics, offering a scalable solution for enhancing autonomy in smart, dynamic environments.

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.

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.

arXiv:2510.23578v1 Announce Type: new Abstract: The rapid adoption of generative artificial intelligence (GenAI) technologies has led many organizations to integrate AI into their products and services, often without considering user preferences. Yet, public attitudes toward AI use, especially in impactful decision-making scenarios, are underexplored. Using a large-scale two-wave survey study (n_wave1=1514, n_wave2=1488) representative of the Swiss population, we examine shifts in public attitudes toward AI before and after the launch of ChatGPT. We find that the GenAI boom is significantly associated with reduced public acceptance of AI (see Figure 1) and increased demand for human oversight in various decision-making contexts. The proportion of respondents finding AI "not acceptable at all" increased from 23% to 30%, while support for human-only decision-making rose from 18% to 26%. These shifts have amplified existing social inequalities in terms of widened educational, linguistic, and gender gaps post-boom. Our findings challenge industry assumptions about public readiness for AI deployment and highlight the critical importance of aligning technological development with evolving public preferences.

arXiv:2507.22030v2 Announce Type: replace-cross Abstract: We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contrast chest CT scans paired with standardized radiology reports from CT-RATE. Construction followed a structured three-stage pipeline. First, GPT-4 was used to extract and standardize findings, descriptors, and metadata from reports originally written in Turkish and machine-translated into English. Second, GPT-4o-mini categorized each finding into a hierarchical ontology of lung and pleural abnormalities. Third, 3D annotations were produced for all CT volumes: the training set was quality-assured by board-certified radiologists, and the validation and test sets were fully annotated by board-certified radiologists. Additionally, a complementary chain-of-thought dataset was created to provide step-by-step hierarchical anatomical reasoning for localizing findings within the CT volume, using GPT-4o and localization coordinates derived from organ segmentation models. ReXGroundingCT contains 16,301 annotated entities across 8,028 text-to-3D-segmentation pairs, covering diverse radiological patterns from 3,142 non-contrast CT scans. About 79% of findings are focal abnormalities and 21% are non-focal. The dataset includes a public validation set of 50 cases and a private test set of 100 cases, both annotated by board-certified radiologists. The dataset establishes a foundation for enabling free-text finding segmentation and grounded radiology report generation in CT imaging. Model performance on the private test set is hosted on a public leaderboard at https://rexrank.ai/ReXGroundingCT. The dataset is available at https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT.

npj Digital Medicine, Published online: 27 October 2025; doi:10.1038/s41746-025-01996-2

Benchmarking large language models for personalized, biomarker-based health intervention recommendations

arXiv:2510.18212v2 Announce Type: replace Abstract: The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

arXiv:2510.15001v2 Announce Type: replace-cross Abstract: We introduce VaultGemma 1B, a 1 billion parameter model within the Gemma family, fully trained with differential privacy. Pretrained on the identical data mixture used for the Gemma 2 series, VaultGemma 1B represents a significant step forward in privacy-preserving large language models. We openly release this model to the community

Nature, Published online: 20 October 2025; doi:10.1038/s41586-025-09713-5

Parity and lactation induce T cell mediated breast cancer protection

Background: Peer support (sharing experiences/support with others with the same condition) improves health outcomes among people with cardiovascular disease (CVD), including self-management behaviours and self-efficacy. However, current peer support interventions are diverse. Evidence is lacking on peer support attenders perceptions of benefits and the elements that are considered priorities, especially for digital interventions. Objective: The study objectives were to 1) describe perceived benefits and recommendations for CVD peer support programs from people attending in-person peer support, 2) identify priorities for digital peer support from consumers and clinicians testing a peer support app prototype, and 3) develop a framework to inform future peer support intervention development. Methods: Qualitative methodology was used across two components to address the objectives of this study. In Component 1, semi-structured focus groups were conducted with attenders of established in-person CVD peer support groups, exploring the perceived benefits of peer support and recommendations for future programs. In Component 2, semi-structured interactive workshops with consumers with CVD and semi-structured online interviews with CVD clinicians/researchers were undertaken seeking feedback and recommendations for digital peer support using an exploratory digital CVD peer support application prototype. Data were recorded digitally, transcribed verbatim, and analysed thematically. Findings from both components were iteratively synthesised to inform a digital peer support development framework. Results: In Component 1, 22 participants (age range 29-84 years, male 45%) took part in focus groups. The overarching theme was that peer support provides benefits through sharing experiences. Five themes were refined and defined; (i) peer support provides a way of coping, (ii) peers learn from each other, (iii) peers understand what each other are going through, (iv) the peer community uplifts mood and build confidence, and (v) awareness, flexibility and resources are important for engagement. In Component 2, five participants (age range 55-74 years, male 60%) attended two workshops and eight clinicians/researchers (age range 30-65 years, male 10%) were interviewed. Three themes were refined and defined: (i) autonomy is essential to promote engagement, (ii) safeguarding is important to both users and clinicians, and (iii) interfaces that are simple, easy to use and visually attractive enable use. Priorities identified from both components included greater peer support awareness and uptake, flexibility with timing and family participation, healthcare professional involvement, provision of resources, autonomous features enabling choice, checklists and clinician moderation for safeguarding, and simple to use interfaces. Conclusions: Participants in peer support programs derive benefit from sharing their experience of living with CVD which enable coping, learning, feeling understood and a sense of community. Priorities were synthesised to create a framework for digital peer support development for future peer support with recommendations to focus on six key areas: uptake, flexibility, resources, autonomy, safeguarding and interface.

Nature Biotechnology, Published online: 16 October 2025; doi:10.1038/s41587-025-02839-x

Somatic small variants in cancer genomes are identified in both short-read and long-read data.

Nature, Published online: 08 October 2025; doi:10.1038/s41586-025-09603-w

MetaGraph enables scalable indexing of large sets of DNA, RNA or protein sequences using annotated de Bruijn graphs.

Background: Cardiovascular disease (CVD) remains the leading cause of death worldwide, yet many web-based sources on cardiovascular (CV) health are inaccessible. Large language models (LLMs) are increasingly used for health-related inquiries and offer an opportunity to produce accessible and scalable CV health information. However, because these models are trained on heterogeneous data, including unverified user-generated content, the quality and reliability of food and nutrition information on CVD prevention remain uncertain. Recent studies have examined LLM use in various health care applications, but their effectiveness for providing nutrition information remains understudied. Although retrieval-augmented generation (RAG) frameworks have been shown to enhance LLM consistency and accuracy, their use in delivering nutrition information for CVD prevention requires further evaluation. Objective: To evaluate the effectiveness of off-the-shelf and RAG-enhanced LLMs in delivering guideline-adherent nutrition information for CVD prevention, we assessed 3 off-the-shelf models (ChatGPT-4o, Perplexity, and Llama 3-70B) and a Llama 3-70B+RAG model. Methods: We curated 30 nutrition questions that comprehensively addressed CVD prevention. These were approved by a registered dietitian providing preventive cardiology services at an academic medical center and were posed 3 times to each model. We developed a 15,074-word knowledge bank incorporating the American Heart Association’s 2021 dietary guidelines and related website content to enhance Meta’s Llama 3-70B model using RAG. The model received this and a few-shot prompt as context, included citations in a Context Source section, and used vector similarity to align responses with guideline content, with the temperature parameter set to 0.5 to enhance consistency. Model responses were evaluated by 3 expert reviewers against benchmark CV guidelines for appropriateness, reliability, readability, harm, and guideline adherence. Mean scores were compared using ANOVA, with statistical significance set at P<.05. interrater agreement was measured using the cohen coefficient and readability estimated flesch-kincaid score. results: llama model scored higher than perplexity gpt-4o models on reliability appropriateness guideline adherence showed no harm.>70%; P<.001 indicated high reviewer agreement. conclusions: the llama model outperformed off-the-shelf models across all measures with no evidence of harm although responses were less readable due to technical language. scored lower on and produced some harmful responses. these findings highlight limitations demonstrate that rag system integration can enhance llm performance in delivering evidence-based dietary information.>

Public health is undergoing profound transformation driven by data from the global health sector and related fields. To address systemic health disparities, scholars and practitioners are increasingly applying a data equity lens, an approach that has become even more urgent as the United States faces the erosion of public health data infrastructure. This paper summarizes insights from an April 2024 convening by the Yale School of Public Health—The Role of Data in Public Health Equity and Innovation—with intersectoral stakeholders from academia, government (local, state, and federal), healthcare, and private industry. The convening included keynote presentations and roundtables regarding the depiction of social determinants of health (SDOH) in data; effects of artificial intelligence (AI) on health data equity; and community-based models for data, providing a framework for cross-cutting discussions. Through a narrative synthesis, themes were identified and synthesized from systematically gathered information from presentations and roundtables. This process led to a set of actionable, cross-cutting recommendations to guide inclusive and impactful data practices for policymakers, public health professionals, and health innovators across diverse contexts: (1) Enable big data and interoperability connecting SDOH and health outcomes; (2) Include diverse, non-technical voices in AI and health discussions; (3) Fund research on data equity and AI in health sciences; (4) Modernize Health Insurance Portability and Accountability Act (HIPAA) with new guidelines for AI and big data; and (5) Research and conceptual frameworks are needed to elucidate interconnections between data equity and health equity.

Nature Medicine, Published online: 06 October 2025; doi:10.1038/s41591-025-03983-2

This Review summarizes recent technical advancements in generative AI, outlines how new models might improve healthcare and discusses validation approaches—using lessons from recent successes and failures in the field.