Beginning in January 2026, the Graduate School will introduce a Spring admission cycle for both the Biomedical Science and Biomedical Data Science and AI master's programs, providing students with greater flexibility in timing and enrollment. Additionally, the Biomedical Data Science and AI (MDSAI) program will expand to include a fully online option, ensuring accessibility to students worldwide, regardless of location.
The Graduate School also has restructured the core curriculum of its Master of Science in Biomedical Science (MSBS) program around more options and greater flexibility for students.
“For 20 years, our MSBS program has successfully prepared our students for future success in advance degree programs,” says Eric Sobie, PhD, Senior Associate Dean for Master’s in Basic Science Programs. “Nonetheless, we recognized that a changing job environment required us to provide students with more options, and the new program structure does just that.”
The program has four distinct tracks that target different types of students and provide greater flexibility for how students can complete the program. It also reduces the total minimum credits from 45 to 36 credits over two, three, or four semesters.
“The trend in master’s programs is to make them more focused and flexible, and we’ve done exactly that with our curriculum changes that enable students to better balance their master’s education with their personal responsibilities, including family and jobs,” says Dr. Sobie, who is also Professor of Pharmacological Sciences at the Icahn School of Medicine at Mount Sinai.
Students can choose a track based on their career goals and stages of life:
Track 1: Post-Baccalaureate Pre-doctoral (pre-PhD or pre-MD-PhD)
This full-time, four-semester track requires students to complete a master’s thesis based on original laboratory research and features a staggered block schedule to facilitate focused study. Students will learn the fundamentals of biomedical sciences while engaging in hands-on research in the laboratories of their chosen principal investigators.Track 2: Post-Baccalaureate Pre-medical (pre-MD)
This full-time, three-semester track also follows a block schedule structure. Students graduate with a capstone project and a final comprehensive examination. This track allows students interested in applying to MD programs to better prepare for the MCAT exam by offering a lighter course load during the spring semester.
Track 3: Industry/Clinical/Professional Development
This flexible track, available in three or four semesters, combines a block schedule alongside a capstone project. It caters to individuals working in the clinical, educational, or private sector who aim to enhance their skills and advance their careers into higher-ranked, better-paid positions.
Track 4: Accelerated Industry/Clinical/Professional Development
This faster-paced version of Track 3 consists of two full-time semesters with a block schedule, a capstone project, and a final comprehensive examination. It is designed for individuals seeking to enhance their skills within a condensed time frame.
“These tracks reflect the understanding that almost all students in this program go on to further education,” says Dr. Sobie, “while those in Biomedical Data Science and AI predominantly move into the workforce after graduation.”
Eric Sobie, PhD, with students.
Master of Science in Biomedical Data Science and AI Program
Another change that captures the new dynamics of a master’s education at Mount Sinai is the renaming of the Biomedical Data Science program to the Master of Science in Biomedical Data Science and AI. It especially reflects the deep penetration artificial intelligence and machine learning have made in the graduate learning experience at Mount Sinai. Fundamentally, the program is structured to develop highly skilled biomedical data scientists who can revolutionize precision medicine.
“We want to build world-class research and learning programs around AI and data-driven innovation, knowing the impact they can have on improving health care,” says Dr. Sobie, Program Director, whose own laboratory uses mathematical models to better understand cardiac physiology. “That requires giving our students the advanced skills they need to analyze the trove of data that’s available, then applying the right computational algorithms that can lead to new drug discovery and development.”
Fortifying this program are four primary research hubs—led by the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine, the foremost department of its kind at any medical school in the country. Its goal is to make the advanced tools and techniques of AI widely available to researchers, clinicians, and students across the Mount Sinai Health System.
The Windreich Department further promotes excellence in biomedical data science education through the Eric and Wendy Schmidt AI in Human Health Fellowship. Fellows are expected to develop solid research projects and apply for National Institutes of Health funding, with the possible goal of becoming assistant professors within the Icahn School of Medicine.
The Mount Sinai Health System, over recent years, has become a recognized leader in the clinical application of AI in the hospital setting.
As one example, the Health System was awarded the 2024 Hearst Health Prize for its creation of NutriScan AI, which was used to facilitate faster identification and treatment of malnutrition in hospitalized patients. The $100,000 Hearst Health Prize, in partnership with the UCLA Center for SMART Health, is awarded to data science programs across the country making an impact in health care.
Working with extensive historical data, a clinical data science team employed more than 80 variables to build a model that significantly outperformed the traditional rule-based model for screening. NutriScan AI has since been deployed at six Mount Sinai hospitals, allowing registered dietitians to prioritize visits to patients to confirm a diagnosis of malnutrition and initiate treatment.