Scientific Computing and Data / AIR·MS (AI Ready Mount Sinai) / News and Updates
News and Updates
Digital Health Partnership Conference – Potsdam 2026
Save-the-dates!
October 12-16, 2026
The Digital Health Partnership Conference – Potsdam 2026, will be at the Hasso-Plattner-Institute in Potsdam, Germany.
Stay tuned for more details!
Digital Health Partnership Conference – New York 2026
May 4-8, 2026
The 2026 Digital Health Partnership Conference brought together researchers, clinicians, trainees, and collaborators from the Hasso Plattner Institute, Mount Sinai Health System, and Data4Life at the Icahn School of Medicine at Mount Sinai.
The week began with a two-day ECG + EHR Hackathon, where teams used AI-Ready Mount Sinai (AIR·MS) and the Minerva High-Performance Computing platform to tackle a real-world clinical AI challenge. The winning team included HPI master’s students Dinh Trung Hieu Le and Chris-Bennet Fleger, and Mount Sinai PhD student Michelle Campoli.
Conference sessions explored how AI, multimodal data, and collaborative science are advancing precision medicine, accelerating research, and transforming clinical care. Discussions also highlighted innovations in audiovisual AI, learning health systems, and discoveries from the Mount Sinai Million Health Discoveries Program.
Thank you to our partners and friends Girish Nadkarni, Alexander Charney, Dara (Meyer) Rozanski, MS, PMP, Kelly Morgan, Emily Holzman, MPH, PMP and the committee for hosting us and organizing this event!
A special thank you goes to Ben Glicksberg, Matteo Danieletto, Ashwin Sawant, Marissa Wirth, Lewis Lo, Malika Gregory, Matt Johnson, Herve DiBello, Anurag Patil and Pablo Guerrero for developing and organizing the #hackathon.
We look forward to seeing you all at the next conference! 🙌
Data2Evidence Cohort Query Tool Training
May 2026
- Session 1 (SLIDES) (VIDEO RECORDING)
- Session 2 (SLIDES) (VIDEO RECORDING)
May 2026
How can large-scale health data help us better understand complex and poorly understood diseases?
Jonas Ebner, Research Assistant and Master’s student at the Hasso Plattner Institute, is using AIR·MS to investigate two challenging topics: Fragile X premutation and post-COVID syndrome (Long COVID). By analyzing electronic health record data with machine learning methods, he aims to identify patterns and potential mechanisms behind these conditions. With access to large, well-structured datasets and high-performance computing, AIR·MS enables Jonas to explore research questions that would be difficult to investigate with traditional study designs.
Read Jonas’ full researcher story and learn how AIR·MS supports his work: Read more
Are you working with AIR·MS?
Share your research story with us, connect with the community, and help shape the future of AI in healthcare. Submit your story and be part of the first 50 researchers to receive an AIR·MS Researcher Trophy as seen in the picture to the left!
Share your AIR·MS researcher story
Publications
“ChatGPT Health performance in a structured test of triage recommendations”
Ashwin Ramaswamy, Alvira Tyagi, Hannah Hugo, Joy Jiang, Pushkala Jayaraman, Mateen Jangda, Alexis E. Te, Steven A. Kaplan, Joshua Lampert, Robert Freeman, Nicholas Gavin, Ashutosh K. Tewari, Ankit Sakhuja, Bilal Naved, Alexander W. Charney, Mahmud Omar, Michael A. Gorin, Eyal Klang, Girish N. Nadkarni.
“Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence”
Samia Shahnawaz, Rhyann Clarke, Suzanne Bakken, Noemie Elhadad, Kyle Landell, Jovita Rodrigues, Matteo Danieletto, Ipek Ensari.
“PhysioJEPA: Joint Embedding Representations of Physiological Signals for Real Time Risk Estimation in the Intensive Care Unit”
Benjamin Fox, Dung Hoang, Joy Jiang, Pushkala Jayaraman, Ankit Parekh, Girish N. Nadkarni, Ankit Sakhuja.
“Large language models are poor clinical administrators: An evaluation of structured queries in real-world electronic health records”
Eyal Klang, Vera Sorin, Panagiotis Korfiatis, Ashwin S. Sawant, Robert Freeman, Alexander W. Charney, Girish N. Nadkarni, Benjamin S. Glicksberg.
Over 220 Participants: AIR·MS Spring 2026 Training Series Sets New Record
April 2026
The AIR·MS Spring 2026 Training Series wrapped up three highly attended sessions in April, drawing over 488 registrations and approximately 220 participants across sessions on Health Data Fundamentals, Large Language Models, and Advanced ML Applications. An average of 83% of surveyed attendees said they would strongly recommend the training to colleagues, and many researchers signed up as new AIR·MS users after joining.
Not yet on board? AIR·MS gives Mount Sinai researchers seamless access to multi-modal clinical data, from EHR and omics to imaging, to accelerate AI-driven discovery. Enroll today: AIR‧MS: Getting Started | Scientific Computing and Data
Appointment of Mr. Herve DiBello as Director of Technology, AI-Ready Mount Sinai (AIR·MS), and Dr. Ashwin Sawant as Medical Director, AI-Ready Mount Sinai (AIR·MS).
March 2026
Herve DiBello brings 30 years of experience in the technology sector as a senior data engineer. Prior to joining Mount Sinai, he spent a significant portion of his IT career as a consultant for Systems, Applications, and Products in Data Processing (SAP), where he worked across various technologies and industries, focusing exclusively on healthcare solutions in recent years. In his new role, Mr. DiBello will lead an engineering team in bringing new data modalities to AIR·MS and serve as chief technical architect for new technology solutions integrated into the platform. His expertise will be pivotal in advancing the technological capabilities of AIR·MS and ensuring its seamless integration with healthcare research initiatives.