Working Group Webinar Library

This Surgical and Procedural Informatics Discussion group will describe challenges and potential solutions to the use of non-human authors in peer-reviewed literature and will introduce emerging evidence that large language models have the potential to change surgical care.

The Student Working Group's (STWG) monthly meeting is a perfect place to get involved! The STWG wants your input on how to improve student involvement through offsite activities, career guidance panels, grant advice sessions, and more! Work together to make AMIA meetings and membership more valuable and rewarding for our future scientists and researchers. Watch the Recording Presenters

Dr. Jackie Gerhart, VP of Clinical Informatics and Epic Clinician shares details on the current state and research potential nation's largest EHR data collaboration platform. Watch the Recording Presenter Jacqueline Gerhart, MD is a family medicine physician and clinical informaticist at Epic in Verona, WI. She earned her medical degree from Mayo Clinic College of Medicine in Rochester, MN, and is a faculty member at the University of Wisconsin-Madison, where she still sees patients, including on the front-lines during this COVID-19 pandemic. Her passion is to reduce barriers to medical care through the best use of technology. She enjoys advocating to align policy with practice to improve clinician well-being and empower patients.

The explosion of biomedical big data and information in the past decade created new opportunities for discoveries to improve the treatment and prevention of human diseases. But the large body of knowledge presents a grand new challenge. Individual scientists around the world are increasingly finding themselves overwhelmed by the sheer volume of research literature and are struggling to keep up to date and to make sense of this wealth of textual information.

Dr. Heidi Hanson discusses a National Cancer Institute (NCI) and Department of Energy collaboration to develop computational modeling and artificial intelligence approaches to advance cancer research. This work is related to the Blue-Ribbon Panel recommendation to build a national cancer data ecosystem.