# Artificial Intelligence and Emerging Technologies in Medicine (AIET) **Webpage:** [https://icahn.mssm.edu/education/phd/biomedical-sciences/artificial-intelligence](/graduate-student-handbook/graduate-student-handbook.md) **Faculty:** #### Description The Artificial Intelligence and Emerging Technologies in Medicine (AIET) concentration of the PhD Program in Biomedical Sciences at ISMMS offers students with solid quantitative and technical backgrounds educational and research opportunities in AI/machine learning, next generation medical technologies (medical devices, sensors, robotics, etc.), imaging, nanotechnology, information technology, and virtual/augmented reality simulation technologies for clinical applications or drug discovery. In addition to receiving foundational education in the use of information systems, students enrolled in the AIET training area will learn how to develop and interpret predictive diagnostic and therapeutic models using a variety of machine learning tools based on statistics and probability theory, drawing upon quantitative fields such as computer science, mathematics, theoretical physics, theoretical/computational chemistry, and digital engineering. AIET further leverages existing relationships with several well-regarded higher education institutions (State University of New York at Stony Brook, Rensselaer Polytechnic Institute (RPI), the Grove School of Engineering at the City College of New York, the Cooper Union - Albert Nerken School of Engineering, and the Hasso Plattner Institute of the University of Potsdam, in Germany) to offer complementary technical expertise to expand collaborative research and enrichment opportunities for trainees and faculty. #### Program Directors Hayit Greenspan, PhD 212-824-8494 Alan C. Seifert, PhD [alan.seifert@mssm.edu]() 212-824-8440 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://ismms-gs.gitbook.io/graduate-student-handbook/chapter-on-multidisciplinary-training-areas/multidisciplinary-training-areas/artificial-intelligence-and-emerging-technologies-in-medicine-aiet.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.