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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Strategic Partnerships & Collaborations
Oracle Health & Baylor College of Medicine
The partners will leverage Oracle’s AI data platform and Real-World Data (RWD), containing over 120 million patient records, to create one of the nation’s largest…
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Sepsis is the leading cause of mortality in burn patients, yet early identification remains difficult due to persistent hyperinflammatory responses and altered baseline physiology. We developed a streamlined machine learning model for early sepsis…
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Although still at a nascent state, quantum computing promises advances in healthcare, from drug discovery to personalised treatments. But it also threatens current cryptographic systems that protect medical data and infrastructure. The concept of…
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Introduction to the New Center
The American Medical Association (AMA) has unveiled a groundbreaking initiative that promises to reshape the future of healthcare technology. The organization announced the establishment of its Center for Digital Health…
Core Clinical & Diagnostic Innovations
Real-World Evidence (RWE) Agent Pilot: Atropos Health, in collaboration with Microsoft, is piloting the Atropos Evidence™ Agent at Stanford Health Care. This expert agent generates patient-specific RWE integrated into the…
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Artificial Intelligence (AI) is revolutionizing biotechnology by accelerating advancements in drug discovery, genomics, medical imaging, and personalized medicine, thereby enhancing efficiency and reducing healthcare costs. This review emphasizes the transformative potential of multimodal AI—systems…
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Reading and summarizing insights from Optical Coherence Tomography (OCT) images is a routine yet time-consuming task that requires expensive time from experienced ophthalmologists. This paper introduces the Multi-label OCT Report Generation (MORG) model, a…
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Surgical site infections (SSIs), among the most frequent healthcare-associated infections, require surveillance, but traditional methods are labour-intensive. We developed machine learning (ML) and rule-based models for the semi-automated detection of deep and organ/space SSIs…
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Speech data inherently contains personally identifiable information. Anonymization strategies to obscure this while preserving essential characteristics all represent a tradeoff between privacy and utility. We examine this balancing act of modifying voice characteristics, masking…
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