Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of clinically relevant health metrics, all in an effort to guide care and ensure the best possible clinical outcomes. However, as the volume and complexity of biomedical data grow, it becomes challenging to effectively process “big data” using conventional techniques. Physicians and scientists must be prepared to look beyond classic methods of data processing to extract clinically relevant information. The purpose of this article is to introduce the modern plastic surgeon to machine learning and computational interpretation of large data sets. What is machine learning? Machine learning, a subfield of artificial intelligence, can address clinically relevant problems in several domains of plastic surgery, including burn surgery; microsurgery; and craniofacial, peripheral nerve, and aesthetic surgery. This article provides a brief introduction to current research and suggests future projects that will allow plastic surgeons to explore this new frontier of surgical science.
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Montreal, Quebec, Canada; Boston, Mass.; and Albany, N.Y.
From the Division of Plastic and Reconstructive Surgery, Faculty of Medicine, McGill University; the Division of Plastic and Reconstructive Surgery, Harvard University, the Division of Plastic and Reconstructive Surgery, Beth Israel Deaconess Medical Center; and the Department of Biological Sciences, University at Albany.
Received for publication May 22, 2015; accepted December 22, 2015.
Disclosure: None of the authors has a financial interest in any of the products, devices, drugs or procedures mentioned in this article.
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Mirko Gilardino, M.D., M.Sc., Montreal Children’s Hospital, 2300 Tupper Street, Room C-1135, Montreal, Quebec H3H 1P3, Canada, email@example.com, Samuel Lin, M.D., Beth Israel Deaconess Medical Center, 110 Francis Street, Suite 5A, Boston, Mass. 02215, firstname.lastname@example.org