Articles
NIH-Funded Research Achieves Key Milestone
Findings by NYITCOM researchers advance understanding of the relationship between menopause and cardiovascular disease risk.
NYITCOM Honors Alumni and Community Partners
The medical school recognized exceptional graduates and supporters at its annual Alumni Awards Dinner, held April 28 at the Garden City Hotel.
Additional Alumni Named to Board of Trustees
Two New York Institute of Technology alumni have been named to the Board of Trustees, the most recent alumni to join the university鈥檚 governing board.
Study: VR Helps Children With Autism Participate in Exercise and Sports
A new study by researchers from the School of Health Professions and College of Osteopathic Medicine demonstrates how virtual reality (VR) can help children with autism spectrum disorder participate in exercise.
A Lasting Impression
While being treated for a serious case of viral meningitis by osteopathic physicians, Chris Kyriakides (D.O. 鈥�89) was compelled to pursue the practice and later inspired his children to follow his path.
Aspiring D.O.s Receive White Coats
The College of Osteopathic Medicine welcomed the Class of 2029 at events in Arkansas and Long Island, where future osteopathic physicians (D.O.s) celebrated the start of their medical education.
The Practice of Care
When he joined the New York State (NYS) Board for Medicine in 2016, Amit M. Shelat (D.O. 鈥�02) recognized it as an opportunity to combine his two passions: medicine and the well-being of his community.
Dedicated to the Pursuit of Knowledge
Students and alumni from the College of Arts and Sciences, School of Health Professions, and College of Osteopathic Medicine shared research findings at impressive industry conferences.
NYITCOM-Arkansas Generates $44.6 Million in Economic Impact
In a recent study, NYITCOM-Arkansas鈥� economic impact supports 263 jobs and operations that result in $2.2 million in additional state and local taxes.
Using AI to Detect ECG Abnormalities
Student-led research uses artificial intelligence (AI) models to interpret abnormalities in electrocardiogram (ECG) test results.