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Artificial Intelligence in knee arthroplasty

Published in N°20 - November / December 2022
Article viewed 51 times

Artificial Intelligence in knee arthroplasty

By Cécile BATAILLER(1), Jobe SHATROV(1), Sébastien PARRATTE(2), Sébastien LUSTIG(1) in category TECHNOLOGY
(1) Orthopaedics surgery and Sports Medicine Department, Croix-Rousse Hospital, Lyon University Hospital, Lyon, France - (2) International Knee and Joint Centre, Abu Dhabi, United Arab Emirates. / [email protected]

Artificial intelligence (AI) is defined as the study of algorithms that give machines the ability to reason and perform cognitive functions such as problem-solving, object, images and word recognition, and decision-making. Over the last 70 years, AI has evolved rapidly with the development of computer models and algorithms designed to replicate human intelligence and perform specific tasks within various industries.

Introduction

Artificial intelligence (AI) is defined as the study of algorithms that give machines the ability to reason and perform cognitive functions such as problem-solving, object, images and word recognition, and decision-making [1]. Over the last 70 years, AI has evolved rapidly with the development of computer models and algorithms designed to replicate human intelligence and perform specific tasks within various industries. Surgeons are key stakeholders in adopting AI-based technologies for medical care. It’s the responsibility of the health-care professionals to guide data scientists and engineers in the development of clinically relevant software. In fact, the goal is to see how AI can help to answer clinically relevant questions and appropriately interpret data to improve patient outcomes. Research and development in this domain are mostly industry driven. As often with new technologies, a rigorous validation process and a clinical relevance analysis are required. The goal of this process is to distinguish which AI-based tool is a clinically relevant tool and which one is just a hype.  Several sections of AI can be used in medical care including analytic models, predictive models and machine learning (ML), natural language processing, robotic, augmented, and mixed...

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