AI

Understanding end-user contexts and identifying design preferences of an artificial intelligence-based clinical decision support tool for early autism detection

Objectives Building on innovations for autism detection—where artificial intelligence (AI)-based models monitor clinical data within electronic health records—this study evaluates the context for clinical decision support (CDS) deployment and identifies design preferences. Materials and Methods This observational study utilized contextual inquiry to elicit perspectives from 8 clinicians and twenty caregivers during 18- to 24-month well-child…

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Aïra: Rethinking AI Research Assistants for Interdisciplinary Science

Scientific discovery increasingly depends on interdisciplinary teams whose members contribute distinct expertise, conceptual frameworks, vocabularies, assumptions, and standards of evidence. Today’s AI research assistants are largely designed to support individual researchers through literature review, writing assistance, coding, and data analysis. While these capabilities improve personal productivity, they provide little support for the collaborative reasoning required…

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AI-powered methods shake up animal testing
Posted onAugust 3, 2026August 3, 2026

Center director Amanda Randles is featured in this Nature Biology article about the FDA’s drive to reduce animal testing in drug development through AI-powered biosimulation techniques such as multi-agent virtual scientists and digital twins.

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Foundation cell segmentation models performance on live microscopy and spatial-omics data

Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including label-free phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the…

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Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling

Patient-specific computational models exhibit strong concordance with invasively measured fractional flow reserve (FFR)—the clinical gold standard for diagnosing coronary ischemia. However, current modeling techniques frequently rely on computationally intensive assumptions such as pulsatile flow dynamics and often fail to optimally leverage patient-specific clinical data that is routinely available, limiting their practical clinical adoption. In this…

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Real-Time Peripheral Revascularization Planning in Chronic Limb Threatening Ischemia Using HarVI: A Digital Twin Approach

Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover…

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Instrumenting Lightweight, Modular Machine Learning Training and Inference

Recent advances in exascale computing have increased the resolution and fidelity of large-scale simulations, while rapid progress in deep learning has accelerated efforts to couple machine learning with physics-based solvers. We present a lightweight, modular in situ coupling methodology that embeds machine learning training and infer-ence directly into simulation workflows using the ParaView and Cat-alyst…

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Artificial Intelligence Prediction of Meningioma Grade Using Preoperative MRI: A Large Multicenter Study

Introduction: Accurate preoperative determination of meningioma grade could substantially enhance surgical planning, yet no reliable noninvasive method currently exists. This study aims to create and externally validate a machine learning model that integrates conventional radiomics with novel fractal and topological metrics to classify WHO meningioma grade from preoperative MRI in a large, multi-institutional cohort with a…

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Real-Time Peripheral Revascularization Planning in Chronic Limb Threatening Ischemia Using HarVI: A Digital Twin Approach

Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover…

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State and Diffusion of National Institutes of Health Funding of AI in Radiology

Artificial intelligence research has profound implications for the future of radiology, making it essential to understand funding patterns and diffusion rate from the National Institutes of Health (NIH), historically the leading source of biomedical research funding in the United States. Recent changes in federal funding further necessitate understanding the trends and focus areas for future…

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