Recent advancements in vision-language models (VLMs) have demonstrated remarkable capabilities across diverse domains. In this talk, we explore the effectiveness of VLMs in a transfer learning setting, where a pre-trained model is fine-tuned on domain specific data. We first introduce PaliGemma 2, a state-of-the-art, open weight VLM from Google with detection and segmentation capabilities. We then present its application to chest X-ray (CXR) interpretation, detailing the adaptation process that achieved state-of-the-art performance on radiology report generation. This talk highlights the potential of VLMs to democratize access to advanced medical image analysis tools with practical guidance on how to leverage them.
Recent advancements in vision-language models (VLMs) have demonstrated remarkable capabilities across diverse domains. In this talk, we explore the effectiveness of VLMs in a transfer learning setting, where a pre-trained model...
Many important healthcare applications like matching patients to clinical trials, applying the right clinical guidelines, differential diagnosis, clinical coding, patient registries, and real-world data curation depend on understanding the full...
In the intricate world of acute care, every step holds the potential to significantly alter a patient’s journey. While some paths lead to reduced hospitalizations and seamless recoveries, others may...
Radiology, long at the forefront of AI adoption in healthcare, is undergoing a profound transformation. The shift from convolutional neural networks (CNNs) to foundation models and vision‑language models (VLMs) is redefining how...
Reliability, accuracy, observability, and auditability are crucial in building LLM workflows in healthcare. All of these rely on the ability to measure LLM automations at scale. But as the metrics we care about...
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