Digital Transformation of Minimally Invasive Surgery Through Artificial Intelligence
Keywords:
Minimally Invasive Surgery Artificial Intelligence (AI) Digital Transformation Robotic-Assisted Surgery Machine Learning Computer Vision Surgical Robotics Clinical Decision Support Real-Time Image Analysis Precision Surgery Postoperative Analytics Digital Health Patient OutcomesAbstract
Minimally invasive surgery (MIS) has significantly advanced surgical care by reducing patient trauma, shortening recovery times, and improving clinical outcomes. The ongoing digital transformation of MIS is being accelerated by the integration of artificial intelligence (AI), which is reshaping surgical planning, intraoperative decision-making, and postoperative care. By leveraging large volumes of surgical, imaging, and patient data, AI enables more precise, efficient, and personalized surgical interventions.
This article examines how AI-driven technologies are transforming minimally invasive surgery across the surgical lifecycle. Machine learning and computer vision algorithms enhance preoperative planning through predictive analytics and patient-specific risk assessment, while intraoperative AI systems support real-time image analysis, instrument tracking, and augmented surgical guidance. In robotic-assisted and laparoscopic procedures, AI improves precision, workflow optimization, and situational awareness, enabling surgeons to operate with greater confidence and control.
The paper also explores the role of AI in postoperative monitoring and outcome analysis, where predictive models identify complications, optimize recovery pathways, and support continuous quality improvement. Architectural considerations—including integration with robotic platforms, edge and cloud computing, data interoperability, and cybersecurity—are discussed, along with regulatory, ethical, and data governance challenges associated with deploying AI in safety-critical clinical environments.
Through analysis of current applications and emerging trends, this article demonstrates how AI is driving the digital transformation of minimally invasive surgery. It concludes that successful adoption requires not only technological innovation but also clinical validation, regulatory alignment, and human-centered design to ensure that AI enhances surgical expertise while maintaining patient safety and trust.
