EchoNet, developed by the Zou Lab, is advanced image post-processing analysis software designed to aid diagnostic review, analysis, and reporting of echocardiographic DICOM images for cardiac function, and was just approved by the FDA. Congrats, team!
A survey article on multimodal models for clinical biomedicine was published in the International Journal of Computer Vision for which DBDS adjunct faculty Dr. Tanveer Syeda-Mahmood is a co-author. Read it here: https://link.springer.com/article/10.1007/s11263-024-02032-8
You’re invited to attend a VC panel on Generative AI and Healthcare on May 17th at 3:00PM. The panel will feature 5 Venture Capitalists and will focus on the latest trends, challenges and success factors with AI in healthcare startups. This panel is open to all faculty, graduate students and postdocs. It is sponsored by DBDS and the course BIODS 295 Generative AI in Healthcare.
When: May 17th, 3:00-4:30pm Panel; 4:30-5:30 Reception
Where: Chem H Building Room E153
Panelists:
In this episode of the AI Grand Rounds podcast, Dr. Nigam Shah, a distinguished Professor of Medicine at Stanford University and inaugural Chief Data Scientist for Stanford Health Care, shares his journey from training as a doctor in India to becoming a leading figure in biomedical informatics in the United States. He discusses the transformative impact of computational tools in understanding complex biological systems and the pivotal role of hashtagArtificialIntelligence in advancing health care delivery, particularly in improving efficiency and addressing systemic challenges. Dr. Shah emphasizes the importance of real-world integration of AI into clinical settings, advocating for a balanced approach that considers both technological capabilities and the systemic considerations of hashtagAIinMedicine. The conversation with NEJM AI Deputy Editors Arjun Manrai, PhD, and Andrew Beam, PhD, also explores the democratization of medical knowledge, why open-source models are under-researched in medicine, and the crucial role of data quality in training AI systems.
Listen to the full episode: https://nejm.ai/ep18