Artificial Intelligence in musculoskeletal medicine: A scoping review

Samantha Cooray; Alexander Deng; Tim Dong; Salah AI Ghazal Hammouche; Matthew Quansah; Jordan Tsigarides; Nicholas Fuggle

Artificial Intelligence in musculoskeletal medicine: A scoping review

Samantha Cooray, Alexander Deng, Tim Dong, Salah AI Ghazal Hammouche, Matthew Quansah, Jordan Tsigarides, Nicholas Fuggle

Research output: Contribution to journal › Article › peer-review

Abstract

In recent years, artificial intelligence (AI) has blossomed via the booming increase in computing power, access to large data sets and the development of novel data science techniques and architectures. This has led to a great interest in the clinical deployment of AI in healthcare. In this review, we look particularly at the field of musculoskeletal medicine and have curated the latest progress in AI and machine learning in the areas of inflammatory arthritis, vasculitis and connective tissue diseases, osteoporosis, Ehlers–Danlos syndrome, osteoarthritis and musculoskeletal surgical interventions. We also highlight the challenges and pitfalls as clinical AI moves forward.

Original language	English
Journal	TouchImmunology
Volume	3
Issue number	2
Early online date	5 Jul 2024
Publication status	E-pub ahead of print - 5 Jul 2024

Access to Document

https://www.touchimmunology.com/musculoskeletal-diseases/journal-articles/artificial-intelligence-in-musculoskeletal-medicine-a-scoping-review/#article-informationLicence: Unspecified

Cite this

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title = "Artificial Intelligence in musculoskeletal medicine: A scoping review",

abstract = "In recent years, artificial intelligence (AI) has blossomed via the booming increase in computing power, access to large data sets and the development of novel data science techniques and architectures. This has led to a great interest in the clinical deployment of AI in healthcare. In this review, we look particularly at the field of musculoskeletal medicine and have curated the latest progress in AI and machine learning in the areas of inflammatory arthritis, vasculitis and connective tissue diseases, osteoporosis, Ehlers–Danlos syndrome, osteoarthritis and musculoskeletal surgical interventions. We also highlight the challenges and pitfalls as clinical AI moves forward.",

author = "Samantha Cooray and Alexander Deng and Tim Dong and {AI Ghazal Hammouche}, Salah and Matthew Quansah and Jordan Tsigarides and Nicholas Fuggle",

note = "Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analysed during the writing of this article. Funding information: No funding was received in the publication of this article.",

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AU - Quansah, Matthew

AU - Tsigarides, Jordan

AU - Fuggle, Nicholas

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N2 - In recent years, artificial intelligence (AI) has blossomed via the booming increase in computing power, access to large data sets and the development of novel data science techniques and architectures. This has led to a great interest in the clinical deployment of AI in healthcare. In this review, we look particularly at the field of musculoskeletal medicine and have curated the latest progress in AI and machine learning in the areas of inflammatory arthritis, vasculitis and connective tissue diseases, osteoporosis, Ehlers–Danlos syndrome, osteoarthritis and musculoskeletal surgical interventions. We also highlight the challenges and pitfalls as clinical AI moves forward.

AB - In recent years, artificial intelligence (AI) has blossomed via the booming increase in computing power, access to large data sets and the development of novel data science techniques and architectures. This has led to a great interest in the clinical deployment of AI in healthcare. In this review, we look particularly at the field of musculoskeletal medicine and have curated the latest progress in AI and machine learning in the areas of inflammatory arthritis, vasculitis and connective tissue diseases, osteoporosis, Ehlers–Danlos syndrome, osteoarthritis and musculoskeletal surgical interventions. We also highlight the challenges and pitfalls as clinical AI moves forward.

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