"Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation"

by Kanevsky, Jonathan; Corban, Jason; Gaster, Richard; Kanevsky, Ari; Lin, Samuel; Gilardino, Mirko (2016)

Abstract

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.

Keywords

No Keywords

Themes

AI and Medicine

Links to Reference

Citation

How to contribute.

Content Copyright 2024 Jean-Philippe Deranty and Macquarie University. This website is licensed under a Creative Commons Attribution Share-Alike Licence (except for journal/book abstracts and quotations which are the property of their respective authors).

Cite this page as though it were a secondary source, thus the inline citation should be (Kanevsky, J., Corban, J., Gaster, R., Kanevsky, A., Lin, S., & Gilardino, M. 2016 p. 890e-897e cited in Deranty et. al. 2024). In the references, use:

For Work / Against Work Debates on the centrality of work