Sensor technology and machine learning to guide clinical decision making in plastic surgery

Abstract

Subjective clinical evaluations are deeply rooted in medical practice. Recent advances in sensor technology facilitate the acquisition of extensive amounts of objective physiological data that can serve as a surrogate for subjective assessments. Along with sensor technology, a branch of artificial intelligence, known as machine learning, has provided decisive advances in several areas of medicine due to its pattern recognition and outcome prediction abilities. The assimilation of machine learning algorithms into sensor technology can substantially improve our current diagnostic and treatment competencies. This review explores available data on the use of sensor technology and machine learning in areas of interest for plastic surgeons, updates current knowledge on the most recent technological advances, and provides a new perspective on the field.

Keywords: Clinical decision making; Machine learning; Postoperative pain; Sensor technology; Surgical flap; Wounds and injury.