Literature DB >> 29656964

Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

Stefano A Bini1.   

Abstract

This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; cognitive computing; deep learning; digital health; digital orthopedics; machine learning

Mesh:

Year:  2018        PMID: 29656964     DOI: 10.1016/j.arth.2018.02.067

Source DB:  PubMed          Journal:  J Arthroplasty        ISSN: 0883-5403            Impact factor:   4.757


  63 in total

Review 1.  Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics.

Authors:  Murali Poduval; Avik Ghose; Sanjeev Manchanda; Vaibhav Bagaria; Aniruddha Sinha
Journal:  Indian J Orthop       Date:  2020-01-13       Impact factor: 1.251

Review 2.  Artificial Intelligence and Orthopaedics: An Introduction for Clinicians.

Authors:  Thomas G Myers; Prem N Ramkumar; Benjamin F Ricciardi; Kenneth L Urish; Jens Kipper; Constantinos Ketonis
Journal:  J Bone Joint Surg Am       Date:  2020-05-06       Impact factor: 5.284

3.  Can machine learning models predict failure of revision total hip arthroplasty?

Authors:  Christian Klemt; Wayne Brian Cohen-Levy; Matthew Gerald Robinson; Jillian C Burns; Kyle Alpaugh; Ingwon Yeo; Young-Min Kwon
Journal:  Arch Orthop Trauma Surg       Date:  2022-05-04       Impact factor: 3.067

4.  The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty.

Authors:  Christian Klemt; Akachimere Cosmas Uzosike; John G Esposito; Michael Joseph Harvey; Ingwon Yeo; Murad Subih; Young-Min Kwon
Journal:  Arch Orthop Trauma Surg       Date:  2022-06-29       Impact factor: 3.067

Review 5.  Moving beyond radiographic alignment: applying the Wald Principles in the adoption of robotic total knee arthroplasty.

Authors:  Jess H Lonner; Graham S Goh
Journal:  Int Orthop       Date:  2022-05-09       Impact factor: 3.075

6.  Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images.

Authors:  Yasmeen George; Bhavna J Antony; Hiroshi Ishikawa; Gadi Wollstein; Joel S Schuman; Rahil Garnavi
Journal:  IEEE J Biomed Health Inform       Date:  2020-12-04       Impact factor: 5.772

Review 7.  Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment.

Authors:  Jun Tan; Feng Qin; Jiuhong Yuan
Journal:  Transl Androl Urol       Date:  2021-04

8.  A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy.

Authors:  Jasminka Hasic Telalovic; Serena Pillozzi; Rachele Fabbri; Alice Laffi; Daniele Lavacchi; Virginia Rossi; Lorenzo Dreoni; Francesca Spada; Nicola Fazio; Amedeo Amedei; Ernesto Iadanza; Lorenzo Antonuzzo
Journal:  Diagnostics (Basel)       Date:  2021-04-28

9.  Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty.

Authors:  Evan M Polce; Kyle N Kunze; Katlynn M Paul; Brett R Levine
Journal:  Arthroplast Today       Date:  2021-02-26

10.  Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study.

Authors:  Jun Ma; Jiani Yang; Yue Jin; Shanshan Cheng; Shan Huang; Nan Zhang; Yu Wang
Journal:  Onco Targets Ther       Date:  2021-05-18       Impact factor: 4.147

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