We help address these challenges through three contributions. First, we publish a new dataset, EHRSHOT, which contains de-identified structured data from the electronic health records (EHRs) of 6,739 patients from Stanford Medicine.

Archaeologists say they located a Black Death burial site containing some of a village's 12,000 dead. Breakthroughs, discoveries, and DIY tips sent six days a week. The Black Death () killed as much as half of Europe's total population between 1346 and 1353, so there are a of bodies buried across the continent. For example, contemporary accounts from Thuringia--a state in central Germany--report that about 12,000 plague victims died around Erfurt amid the city's outbreak in 1350. But despite multiple accounts attesting to this devastation, none of the 11 mass graves could be pinpointed for centuries.

Intestinal parasites that still plague us today were all over Roman Britain. Breakthroughs, discoveries, and DIY tips sent every weekday. The soldiers guarding the Roman Empire's northwestern frontier had a real parasite problem. Scientists analyzing the sewer drains from the Roman fort Vindolanda (near Hadrian's Wall in northern England) found three types of intestinal parasites --roundworm,whipworm, and . The findings published in the journal mark the first time that has been documented in Roman Britain.

More than just rats and fleas added to the'perfect storm' plague. Photograph of the fresco Trionfo della Morte, taken at its original location in the Camposanto Monumentale in Pisa. The fresco, known as the "Triumph of Death" and attributed to the painter Buonamico Buffalmacco, is not precisely dated; scholarly estimates range from 1335 to 1350. While it does not depict the Black Death explicitly, the selected detail shows victims of an epidemic from diverse social backgrounds, their souls carried off by demons. Breakthroughs, discoveries, and DIY tips sent every weekday.

High-quality and carefully curated data is a cornerstone of training medical large language models, as it directly impacts both generalization and robustness to unseen clinical tasks. We investigate strategies for training and data curation to develop a robust multimodal reasoning model in the medical domain. Our work focuses on supervised fine-tuning (SFT) and explores data recipes that leverage structured reasoning traces. Using our proposed data recipe, we scale experiments to a dataset of over 8 million examples and 6.8 billion response tokens, achieving state-of-the-art performance among open-source models across diverse out-of-distribution medical benchmark tasks. Our results further indicate that curating a high-quality, diverse training dataset with varying structured reasoning trace lengths enables the fine-tuned model to self-calibrate its reasoning trajectory lengths based on the downstream task, without explicit supervision. We present key insights, describe the data curation strategy, and outline next steps toward developing robust medical vision-language reasoning system.

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.

A decade of rapid advances in artificial intelligence (AI) has opened new opportunities for clinical decision support systems (CDSS), with large language models (LLMs) demonstrating strong reasoning abilities on timely medical tasks. However, clinical texts are often degraded by human errors or failures in automated pipelines, raising concerns about the reliability and fairness of AI-assisted decision-making. Y et the impact of such degradations remains under-investigated, particularly regarding how noise-induced shifts can heighten predictive uncertainty and unevenly affect demographic subgroups. We present a systematic study of state-of-the-art LLMs under diverse text corruption scenarios, focusing on robustness and equity in next-visit diagnosis prediction. To address the challenge posed by the large diagnostic label space, we introduce a clinically grounded label-reduction scheme and a hierarchical chain-of-thought (CoT) strategy that emulates clinicians' reasoning. Our approach improves robustness and reduces subgroup instability under degraded inputs, advancing the reliable use of LLMs in CDSS.

Covariate-adjusted response-adaptive randomization (CARA) designs are gaining increasing attention.