Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a…
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Multimodal models have enabled the integration of digital pathology, radiology, clinical information, and omics data to enhance Colorectal cancer (CRC) care. This systematic review critically appraises Multimodal digital pathology techniques applied in CRC, their…
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The complexity and variability of high-resolution pathological images present significant challenges in computational pathology. While AI-driven pathology foundation models have advanced the field, they require large-scale datasets, substantial storage, and significant computational resources, as…
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Artificial Intelligence has revolutionized healthcare by offering smart services and reducing diagnostic burden, particularly facilitating the identification and segmentation of malignant tissues. However, current task-specific approaches require disease-specific models, while universal foundation models demand…
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Harmonized ONcologY Biomedical Embedding Encoder (HONeYBEE) is an open-source framework that integrates multimodal biomedical data for oncology applications. It processes clinical data (structured and unstructured), whole-slide images, radiology scans, and molecular profiles to generate…
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Carotid atherosclerosis is a key predictor of cardiovascular disease (CVD), necessitating early detection. While foundation models (FMs) show promise in medical imaging, their optimal selection and fine-tuning strategies for classifying carotid atherosclerosis from retinal…
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Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing AI models often requires extensive annotation, and existing models tend to…
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The rise of biomedical foundation models creates new hurdles in model testing and authorization, given their broad capabilities and susceptibility to complex distribution shifts. We suggest tailoring robustness tests according to task-dependent priorities and…
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Clinicians spend significant time reviewing medical images and transcribing findings. By integrating visual and textual data, foundation models have the potential to reduce workloads and boost efficiency, yet their practical clinical value remains uncertain….
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What You Should Know:
– Mayo Clinic announced a pivotal step toward integrating AI solutions in the clinical setting with the deployment of NVIDIA DGX SuperPOD with NVIDIA DGX B200 systems.
– The advanced infrastructure provides…
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