AI at VP&S Initiative Kickoff Features AI-Driven Research, Applications, and Innovations
AI is revolutionizing medicine—enhancing clinical decision-making, improving patient care, and transforming healthcare delivery. The AI at VP&S Initiative aims to empower clinicians and researchers with the knowledge, tools, and training needed to harness AI’s full potential. From groundbreaking research to hands-on education, we are building an AI-literate healthcare workforce ready to lead in an era where those who understand and apply AI will shape the future of medicine.
All members of the University are welcome to attend the AI at VP&S Initiative Kickoff, a showcase of AI-driven research, applications, and innovations at VP&S. This event offers a unique opportunity to learn about the newly launched AI initiative, experience real-time demonstrations highlighting the diverse AI use cases across VP&S, and connect with potential collaborators, mentors, and colleagues interested in AI.
AI at VP&S Kickoff Details
Date: Monday, Feb. 24, 2025
Time: 5:00 – 7:00 pm
Location: Milstein Building, 177 Fort Washington, Ave.
Room: Heart Center Auditorium
AI at VP&S Kickoff List of Demos
Radiation Oncology Clinical Trial GPT
Adam Riegel
Radiation Oncology
ChartR: Transparent AI you can Trust
Akash Kapoor
VP&S Medical Students
Automated Generation of Brief Hospital Course Documentation
Karthik Natarajan, Jason Zucker
Biomedical Informatics, Infectious Diseases
Leveraging AI for a Better Note
Leah Katz
Radiation Oncology
Standardized Patient (OSCE) Chatbot
Hannah Weinstein, Oliver Piltch
VP&S Medical Students
CONCERN Early Warning System
Rachel Lee, Sarah Rossetti
Biomedical Informatics
The STAR System (Sperm Tracking and Recovery) at Columbia University Fertility Center
Zev Williams
Obstetrics and Gynecology
Synthetic Data Generation of Longitudinal Patient Records
Chao Pang, Karthik Natarajan
Biomedical Informatics
Faster R-CNN Detection of Pediatric Upper Extremity Fractures
John Zech
Radiology
Using Augmented Reality to Visualize and Interact with a Cross-Attention Enhanced 3D Optical Coherence Tomography Volume for Ophthalmic Disease Diagnosis
Kaveri Thakoor
Ophthalmology
Machine Learning for Automated Ultrasound Guided Infant Lumbar Puncture
Kanisha Shah, David Kessler
Emergency Medicine
Automatic DCIS Segmentation in First Post-Contrast DCE-MRI Image using MA-SAM architecture with pre-trained SAM backbone
Aaron Sossin, Despina Kontos
Radiology
Mining the Health Disparities and Minority Health Bibliome
Harry Reyes Nieva, Noémie Elhadad
Biomedical Informatics
OphthoACR (Ophthalmology Automated Chart Review): An AI-Powered Tool for Complete Automation of Ophthalmology Chart Reviews and Cohort Data Analysis
Karen Chen, Leejee Suh
Ophthalmology
SpeechCARE: A Novel Multimodal Fusion Transformer Model for Early Detection of Cognitive Impairment–NIA Alzheimer’s Challenge
Maryam Zolnoori
Neurology
Secure Clinical Decision Making in Genomic Medicine
Jacob Blindenbach, Gamze Gursoy
Biomedical Informatics
dpFeMIA: A Decentralized Personalization Framework for Federated Medical Image Analysis
Jingyun Chen, Yading Yuan
Radiation Oncology