Enhancing Minimally Invasive Surgery with Deep Learning-Based Medical Robotics
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Keywords

Medical Robotics
Surgical Efficiency
Surgical Robotics

How to Cite

[1]
Dr. Carlos Diaz, “Enhancing Minimally Invasive Surgery with Deep Learning-Based Medical Robotics: Utilizes deep learning algorithms to enhance the capabilities of medical robotics for performing precise and minimally invasive surgical procedures, improving patient outcomes and recovery times”, Journal of Bioinformatics and Artificial Intelligence, vol. 3, no. 2, pp. 253–262, Oct. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/106

Abstract

Minimally invasive surgery (MIS) has revolutionized the field of surgery by offering patients shorter recovery times, reduced pain, and fewer complications compared to traditional open surgeries. The advent of medical robotics has further advanced MIS by providing surgeons with enhanced precision and control during procedures. However, challenges such as complex anatomical structures and hand-eye coordination still exist. Deep learning, a subset of artificial intelligence, has emerged as a promising tool to address these challenges and improve the capabilities of medical robotics in MIS. This paper explores the application of deep learning algorithms in enhancing medical robotics for MIS, highlighting their potential to improve patient outcomes and revolutionize surgical practices.

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