Hear talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and healthcare.

We have built AI-driven tools to automate the assessment of key heart parameters from point-of-care ultrasound, including Right Atrial Pressure (RAP) and Ejection Fraction (EF). In collaboration with UCSF, we trained deep learning models on a proprietary dataset of over 15,000 labeled ultrasound studies and deployed the full pipeline in a real-time iOS app integrated with the Butterfly probe. A UCSF-led clinical trial has validated the RAP workflow, and we are actively expanding the system to support EF prediction using both A4C and PLAX views.

This talk will present our end-to-end pipeline, from dataset development and model training to mobile deployment—demonstrating how AI can enable real-time heart assessments directly at the point of care.

In hospitals, direct patient observation is limited–nurses spend only 37% of their shift engaged in patient care, and physicians average just 10 visits per hospital stay. LookDeep Health's AI-driven platform enables continuous and passive monitoring of individual patients, and has been deployed "in the wild" for nearly 3 years. They recently published a study validating this system, titled "Continuous Patient Monitoring with AI" (https://lookdeep.github.io/ai-norms-2024/). This talk is a technical dive into said paper, focusing on the intersection of AI and real-world application.

Artificial intelligence is rapidly transforming how we diagnose, treat, and manage health.

We present a multimodal AI pipeline to streamline patient selection and quality assessment for radiology AI development. Our system evaluates patient clinical histories, imaging protocols, and data quality, embedding results into imaging metadata. Using FiftyOne researchers can rapidly filter and assemble high-quality cohorts in minutes instead of weeks, freeing radiologists for clinical work and accelerating AI tool development.