June 26 - Visual AI in Healthcare

Join us for one of the several virtual events focused on the latest research, datasets and models at the intersection of visual AI and healthcare.

When

June 26 at 9 AM Pacific

Where

Online. Register for the Zoom!

Multimodal AI for Efficient Medical Imaging Dataset Curation

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.

About the Speaker

Brandon Konkel is a Senior Machine Learning engineer at Booz Allen Hamilton with over a decade of experience developing AI solutions for medical imaging.

AI-Powered Heart Ultrasound: From Model Training to Real-Time App Deployment

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.

About the Speaker

Jeffrey Gao is a PhD candidate at Caltech, working at the intersection of machine learning and medical imaging. His research focuses on developing clinically deployable AI systems for ultrasound-based heart assessments, with an emphasis on real-time, edge-based inference and system integration.

Let’s Look Deep at Continuous Patient Monitoring

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”. This talk is a technical dive into said paper, focusing on the intersection of AI and real-world application.

About the Speaker

Paolo Gabriel, PhD is a senior AI engineer at LookDeep Health, where they continue to use computer vision and signal processing to augment patient care in the hospital.

AI in Healthcare: Lessons from Oncology Innovation

About the Speaker

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

Dr. Asba (AT) Tasneem is a healthcare data and innovation leader with over 20 years of experience at the intersection of clinical research, AI, and digital health. She has led large-scale programs in oncology and data strategy, partnering with organizations like the FDA, Duke University, and top pharma companies to drive AI-enabled healthcare solutions.