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For heart disease, early diagnosis is vital for saving lives. For heart disease, early diagnosis is vital for saving lives. Doctors have developed a "superhuman" AI tool that can spot heart disease in less than two seconds. The groundbreaking technology has been trained on millions of patients and works by extracting more information from a routine electrocardiogram (ECG) than the human eye can typically see. The traditional ECG, which records electrical activity in the heart, including the rate and rhythm, has been a vital medical tool in diagnosing heart attacks and abnormal heart rhythms for a century.

This actor was told he had Alzheimer's, now he's performing a one-man show Image caption, Peter Marinker, who was diagnosed with Alzheimer's disease two years ago, began dress rehearsals for the Beckett play Krapp's Last Tape in June For four days running, Peter Marinker had arrived at rehearsals that were not even taking place. Dementia had muddled the dates in his mind. Yet he is now preparing to do something most actors would find daunting even with perfect recall: performing a much-loved Samuel Beckett play alone on a London stage. Marinker, 85, is best known for his voice work: from radio plays and video games to big-budget films like Labyrinth and Paddington in Peru. On camera he was the adviser to the US president in Love Actually and one of the airline passengers in the drama United 93.

Asbestos killed my mum in her 40s - was her school to blame? To play this video you need to enable JavaScript in your browser. Figure caption, 'Mum died young from asbestos - now my family want justice' When Caroline Bryan was enjoying high school with friends in the early 90s, she would never have imagined replaying those memories in her final weeks, while searching for answers about her terminal condition. The mum was 46 when she died from mesothelioma, an incurable cancer linked to asbestos exposure. As she came to terms with her difficult diagnosis, Caroline's belief was the only place she could have encountered asbestos was during her time at school decades earlier.

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Recent advances in computational pathology have led to the emergence of numerous foundation models. These models typically rely on general-purpose encoders with multi-instance learning for whole slide image (WSI) classification or apply multimodal approaches to generate reports directly from images. However, these models cannot emulate the diagnostic approach of pathologists, who systematically examine slides at low magnification to obtain an overview before progressively zooming in on suspicious regions to formulate comprehensive diagnoses.

Foundation models trained on large-scale de-identified electronic health records (EHRs) hold promise for clinical applications. However, their capacity to memorize patient information raises important privacy concerns. In this work, we introduce a suite of black-box evaluation tests to assess privacy-related memorization risks in foundation models trained on structured EHR data. Our framework includes methods for probing memorization at both the embedding and generative levels, and aims to distinguish between model generalization and harmful memorization in clinically relevant settings. We contextualize memorization in terms of its potential to compromise patient privacy, particularly for vulnerable subgroups.

Clinical reasoning in medicine is a hypothesis-driven process where physicians refine diagnoses from limited information through targeted history, physical examination, and diagnostic investigations. In contrast, current medical benchmarks for large language models (LLMs) primarily assess knowledge recall through single-turn questions, where complete clinical information is provided upfront. To address this gap, we introduce VivaBench, a multi-turn benchmark that evaluates sequential clinical reasoning in LLM agents. Our dataset comprises 1152 physiciancurated clinical vignettes structured as interactive scenarios that simulate a viva voce examination in medical training, requiring agents to actively probe for relevant findings, select appropriate investigations, and synthesize information across multiple steps to reach a diagnosis. We evaluated several state-of-the-art LLMs and found that while models demonstrate competence in diagnosing conditions within well-described clinical presentations, their performance degrades significantly when required to navigate diagnostic uncertainty. Our analysis identified several failure modes that mirror common issues in clinical practice, including: (1) fixation on initial hypotheses, (2) excessive investigation ordering, (3) premature diagnostic closure, and (4) missing critical conditions. These patterns reveal fundamental limitations in how current LLMs manage uncertainty and gather information sequentially. Through VivaBench, we provide a standardized benchmark for evaluating conversational medical AI systems for real-world clinical decision support. Beyond medical applications, we contribute to the larger corpus of research on agentic AI by demonstrating how sequential reasoning trajectories can diverge in complex decision-making environments.

Clinical diagnosis is a highly specialized discipline requiring both domain expertise and strict adherence to rigorous guidelines. While current AI-driven medical research predominantly focuses on knowledge graphs or natural text pretraining paradigms to incorporate medical knowledge, these approaches primarily rely on implicitly encoded knowledge within model parameters, neglecting task-specific knowledge required by diverse downstream tasks. To address this limitation, we propose Retrieval-Augmented Diagnosis (RAD), a novel framework that explicitly injects external knowledge into multimodal models directly on downstream tasks. Specifically, RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guidelineenhanced contrastive loss that constrains the latent distance between multi-modal features and guideline knowledge, and the dual transformer decoder that employs guidelines as queries to steer cross-modal fusion, aligning the models with clinical diagnostic workflows from guideline acquisition to feature extraction and decision-making. Moreover, recognizing the lack of quantitative evaluation of interpretability for multimodal diagnostic models, we introduce a set of criteria to assess the interpretability from both image and text perspectives. Extensive evaluations across four datasets with different anatomies demonstrate RAD's generalizability, achieving state-of-the-art performance. Furthermore, RAD enables the model to concentrate more precisely on abnormal regions and critical indicators, ensuring evidence-based, trustworthy diagnosis. Our code is available at this repository.

Section C introduces5 the three main tasks defined in our benchmark: abnormality detection, radiological image captioning,6 and clinical reasoning through differential diagnosis.