With an aim to improve patient outcomes and medical care, researchers continue to explore the use of machine learning.

Considered a subfield of artificial intelligence, machine learning could be a way to handle the growing volume of electronic data information present in the healthcare industry. The May issue of Plastic and Reconstructive Surgery, the medical journal of the American Society of Plastic Surgeons (ASPS), provides an overview of machine learning and a glimpse of how it could contribute to advances in plastic surgery.

“Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision making,” Jonathan Kanevsky, MD, of McGill University, Montreal, and colleagues say in a media release from Wolters Kluwer Health. “They highlight some key areas in which machine learning and Big Data could contribute to progress in plastic and reconstructive surgery.”

Machine learning uses historical data to create algorithms with the ability to acquire knowledge, according to Kanevsky and colleagues in the release. Examples of successful machine learning include the IBM Watson Health cognitive computing system and the National Surgical Quality Improvement Program used by the American College of Surgeons.

The authors believe plastic surgery can also benefit from “objective and data-driven machine learning approaches” and hope to apply machine learning with the ASPS’s Tracking Operations and Outcomes for Plastic Surgeons’ database.

Five areas show particular promise in improving efficiency and outcomes in the industry. These areas include burn surgery, hand and peripheral nerve surgery, microsurgery, craniofacial surgery, and aesthetic surgery.

Another application of machine learning could be in the area of plastic surgery training, but the authors stressed computer-generated algorithms will not replace the human eye.

“These are tools that not only may help the decision-making process but also find patterns that might not be evident in analysis of smaller data sets of anecdotal experience,” Kanevsky and co-authors conclude, per the release. “By embracing machine learning, modern plastic surgeons may be able to redefine the specialty while solidifying their role as leaders at the forefront of scientific advancement in surgery.”

[Source(s): Wolters Kluwer Health, Science Daily]