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AI in Medicine for Medical Trainees

AI in Medicine Focus Area Curriculum Requirements

Increasingly, the decisions physicians make about how best to treat their patients will be informed by the results of computational analyses of patient data. This increasing reliance on methods of artificial intelligence to guide patient care will not only transform medicine, but will also transform the ways in which physicians are trained. Future physicians will need to understand core principles of data science and be able to apply them to critically evaluate the emerging literature on AI in medicine and to do research in this emerging field.

Over the course of two semesters, the AI in Medicine focus area provides medical students, residents, and clinical fellows with the advanced training needed to think critically about topics in data science, and to pursue careers in research or development. Training in this focus area can be individually tailored to meet the needs of medical trainees from diverse educational backgrounds. Basic course requirements in this focus area allow for maximum flexibility in curriculum design.

Limited scholarship funds may be available by application to AI in Medicine students who are currently affiliated with the Johns Hopkins Medical Institutions.

Two males students are in scrubs and working with a medical imaging device.
AIM Focus Area Courses
  • Biomedical Data Science (EN.580.475)
  • Biomedical Data Science Lab (EN.580.477)
  • Biomedical Data Design I and II (EN.580.697 and EN.580.638)
  • Precision Care Medicine I and II (EN.580.680/681)
AIM Focus Area Electives
  • Artificial Intelligence System Design & Development (EN.601.686)
  • Clinical Data Analysis with Python (ME.600.720)
  • Computational Molecular Medicine (EN.553.650)
  • Computer Integrated Surgery I (EN.601.655 (01))
  • Computer Vision (EN.601.661)
  • Data Mining (EN.553.636)
  • Deep Learning (EN.520.638)
  • Deep Learning: Data to Models (EN.601.676)
  • Deep Learning for Automated Discourse (EN.601.767)
  • Deep Learning in Discrete Optimization (EN.553.667)
  • Foundations of Computational Biology and Bioinformatics (EN.580.688)
  • Introduction to Computational Medicine (EN580.631)
  • Introduction to Probability (EN.553.620)
  • Introduction to Statistics (EN.553.630)
  • Learning, Estimation and Control (EN.580.691)
  • Machine Learning (EN.601.675)
  • Machine Learning I & II (EN.553.740/1)
  • Machine Learning: Deep Learning (EN.601.682)
  • Machine Learning for Signal Processing (EN.520.612)
  • Mathematics of Deep Learning (EN.580.745)
  • Sparse Representations in Computer Vision & Machine Learning (EN.580.709)
  • Vision as Bayesian Inference (EN.601.783)
  • Spring (3rd and 4th terms through the School of Public Health) – You must register for both:
    • Data Science for Public Health I (PH.140.628.71)
    • Data Science for Public Health II (PH.140.629.71)
    • *Instructor and advisor approvals required with the submission of an interdivisional registration form through SEAM

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