N Nature Medicine · Nov 05, 2025 A multimodal whole-slide foundation model for pathology The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning. However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose Transformer-based pathology Image and Text Alignment Network (TITAN), a multimodal whole-slide foundation model pretrained using 335,645 whole-slide images via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any fine-tuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that it outperforms both ROI and slide foundation models across machine learning settings, including linear probing, few-shot and zero-shot classification, rare cancer retrieval, cross-modal retrieval and pathology report generation. Machine learning Pathology
N Nature Medicine · Oct 27, 2025 A full life cycle biological clock based on routine clinical data and its impact in health and diseases Aging research has primarily focused on adult aging clocks, leaving a critical gap in understanding a biological clock across the full life cycle, particularly during infancy and childhood. Here we introduce LifeClock, a biological clock model that predicts biological age across all life stages using routine electronic health records and laboratory test data. To enhance individualized predictions, we integrated virtual patient representations from 24,633,025 heterogeneous longitudinal clinical visits across 9,680,764 individuals and projected them into a latent space. Our approach leverages EHRFormer, a time-series transformer-based model, to analyze developmental and aging dynamics with high precision and develop accurate biological age clocks spanning infancy to old age. Our findings reveal distinct biological clock patterns across different life stages. The pediatric clock is strongly associated with children’s development and accurately predicts current and future risks of major pediatric diseases, including malnutrition, growth and developmental abnormalities. The adult clock is strongly associated with aging and accurately predicts current and future risks of major age-related diseases, such as diabetes, renal failure, stroke and cardiovascular diseases. This work therefore distinguishes pediatric development from adult aging, establishing a novel framework to advance precision health by leveraging routine clinical data across the entire lifespan. Ageing Data mining Machine learning biology
N Nature Medicine · Aug 28, 2025 Influenza vaccine strain selection with an AI-based evolutionary and antigenicity model Current vaccines provide limited protection against rapidly evolving viruses. For example, Centers for Disease Control and Prevention estimates show that the overall influenza vaccine effectiveness against outpatient illness in the United States averaged below 40% between 2012 and 2021. Moreover, the clinical outcomes of a vaccine can be assessed only retrospectively. Here we propose an in silico method named VaxSeer that predicts the antigenic match of vaccine candidates with circulating viruses, in the context of the viruses’ relative dominance in the future influenza season. Based on 10 years of retrospective evaluation using sequencing and antigenicity data, our approach consistently selects strains with better empirical antigenic matches to circulating viruses than annual recommendations. Finally, our predicted estimate of antigenic match exhibits a strong correlation with influenza vaccine effectiveness and reduction in disease burden, highlighting the promise of this framework to drive the vaccine selection process. Epidemiology Machine learning Vaccines Immunology Machine Learning Human Drug Development
N Nature Medicine · Aug 20, 2025 AI-based diagnosis of acute aortic syndrome from noncontrast CT The accurate and timely diagnosis of acute aortic syndrome (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic computed tomography (CT) angiography is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo noncontrast CT as the initial imaging testing, and CT angiography is reserved for those at higher risk. Although noncontrast CT can reveal specific signs indicative of AAS, its diagnostic efficacy when used alone has not been well characterized. Here we present an artificial intelligence-based warning system, iAorta, using noncontrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multicenter retrospective study (n= 20,750), iAorta achieved a mean area under the receiver operating curve of 0.958 (95% confidence interval 0.950–0.967). In the large-scale real-world study (n= 137,525), iAorta demonstrated consistently high performance across various noncontrast CT protocols, achieving a sensitivity of 0.913–0.942 and a specificity of 0.991–0.993. In the prospective comparative study (n= 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway for patients with initial false suspicion from an average of 219.7 (115–325) min to 61.6 (43–89) min. Furthermore, for the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under noncontrast CT protocol in the emergency department. For these 21 AAS-positive patients, the average time to diagnosis was 102.1 (75–133) min. Finally, iAorta may help prevent delayed or missed diagnoses of AAS in settings where noncontrast CT remains the only feasible initial imaging modality—such as in resource-limited regions or in patients who cannot receive, or did not receive, intravenous contrast. Aortic diseases Computed tomography Machine learning Machine Learning Clinical Human
