Applications of machine learning using electronic medical records in spine surgery

John T. Schwartz, Michael Gao, Eric A. Geng, Kush S. Mody, Christopher M. Mikhail, Samuel K. Cho

Research output: Contribution to journalReview articlepeer-review

25 Scopus citations


Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.

Original languageEnglish
Pages (from-to)643-653
Number of pages11
Issue number4
StatePublished - Dec 2019


  • Artificial intelligence
  • Deep learning
  • Electronic medical records
  • Machine learning
  • Spine surgery


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