We're excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Tiffany Callahan, Lead Architect of Agentic AI Systems at SandboxAQ. As a…
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Remote photoplethysmography (rPPG) offers a non-invasive means of estimating heart rate in telemedicine settings. Yet its reliability remains uncertain due to the limited diversity and ecological validity of existing benchmark datasets. In this work,…
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Vision-language models (VLMs) show promise for answering clinically relevant questions, but their robustness to medical image artefacts remains unclear. We evaluated VLMs’ robustness through their performance on images with and without weak artefacts across…
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Careful selection of skin lesions that require expert evaluation is important for early skin cancer detection. Yet challenges include lack of cost-effective asymptomatic screening, geographical inequality in access to specialty dermatology, and long wait…
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Current AI approaches for cardiac diagnosis require condition-specific supervised learning with extensive labeled datasets, leading to fundamental scalability barriers. We developed an ECG-CLIP model, applying contrastive multimodal learning to enable zero-shot cardiac diagnosis from…
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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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Current self-supervised learning (SSL) methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and scalability. We present 3DINO, a cutting-edge SSL method adapted to 3D…
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Cardiovascular diseases remain the leading cause of death in developed countries. This study introduces deep learning ensembles for arrhythmia detection and atrial fibrillation (AF) recurrence prediction from electrocardiogram scans, supported by explainable artificial intelligence…
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Biased and poorly documented dermatology datasets pose risks to the development of safe and generalizable artificial intelligence (AI) tools. We created a Dataset Nutrition Label (DNL) for multiple dermatology datasets to support transparent and…
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Ed Gaudet, CEO and Founder of Censinet
Artificial intelligence (AI) is the ultimate double-edged sword in healthcare. On one side, AI is already driving real improvements, from accelerating diagnostic imaging to streamlining operational workflows such…
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