Hosted by the AMIA 25x5 Taskforce, this webinar explores Cedars-Sinai’s implementation of an AI-powered voice dictation technology to enhance nursing workflows by reducing administrative burden, improving efficiency, and elevate the patient and staff experience.
Help clinicians get back to seeing patients! Join us for a webinar on cutting-edge strategies to combat alert fatigue and other clinical decision support (CDS) burden. Discover how benchmarks and advanced analytics can reduce variation and eliminate inefficiencies in native CDS systems.
This dynamic discussion will feature perspectives from academia, clinical practice, and industry, highlighting practical strategies for integrating AI solutions into healthcare settings. Panelists will share experiences, challenges, and lessons learned from implementation efforts, with a focus on governance, workflow integration, and impact assessment. Attendees can expect to gain valuable insights into translating AI research into effective clinical practice.
Large Language Models Are Less Effective at Clinical Prediction Tasks Than Locally Trained Machine Learning Models
Alert Design in The Real World: A Cross-Sectional Analysis of Interruptive Alerting at 9 Academic Pediatric Health Systems
As part of the Department of Energy’s partnership with the National Cancer Institute, the Modeling Outcomes Using Surveillance Data and Scalable AI for Cancer (MOSSAIC) project aims to develop deep learning models to support near-real-time cancer surveillance at the population level. Through the NCI’s Surveillance, Epidemiology, and End Results (SEER) program, we deploy models for the automated coding of cancer cases across state and regional cancer registries throughout the United States.
In this talk, James Zou, PhD explores how generative AI agents can enable drug discovery and development. He introduces the Virtual Lab—a collaborative team of AI scientist agents conducting in silico research meetings to tackle open-ended R&D projects. The Virtual Lab designed new nanobody binders to recent COVID variants, which were experimentally validated. He then discusses some interesting opportunities in designing and optimizing multi-agent interactions.
The Board of Directors is the primary governance body of the Association. Its members focus on high-level strategy, oversight, and accountability for the organization and its operations.
Developing equitable, sustainable informatics solutions is key to scalability and long-term success for projects in the global health informatics (GHI) domain. This webinar presents key strategies for incorporating principles of health equity in the GHI project lifecycle.
Traditional machine learning classifiers using structured representations of text, such as randomly initialized concept embeddings (concept unique identifiers - CUIs), have demonstrated strong performance in clinical risk prediction tasks. In prior work, we developed a CUI-based convolutional neural network substance misuse classifier trained on clinical notes for hospital-based screening. While effective, such models require extensive feature engineering and are limited in their semantic understanding. Recent advances in large language models (LLMs) enable richer contextualization of clinical narratives through prompt engineering and parameter-efficient tuning for computable phenotyping.