Peng, Junjie;
Robinson, George A;
Jury, Elizabeth;
Donnes, Pierre;
Ciurtin, Coziana;
(2023)
AI in Rheumatology.
In: Krittanawong, Chayakrit, (ed.)
Artificial Intelligence in Clinical Practice: How AI Technologies Impact Medical Research and Clinics.
Elsevier: Amsterdam, Netherlands.
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Abstract
Autoimmune rheumatic diseases (ARDs) are characterised by chronic inflammation driven by abnormal immune responses reflected in a significant heterogeneity in clinical presentation and a multitude of disease and organ specific biomarkers. Recent advances in research try to make use of various tests and patient and disease related outcomes to improve disease recognition, and support personalised therapeutic interventions, ultimately aiming for improved patient outcomes. Artificial intelligence (AI) is a broad terminology comprising various analytical models that aim to mimic the function of the human brain by learning from existing knowledge. Machine learning (ML) is one of the most important AI methods, which has the ability to handle high-dimensional data, automatically without human intervention, allowing for easy pattern identification in a wide range of applications. The clinical need to identify patterns of response to treatment or predict disease flares as well as damage accrual in ARDs has led to the development of various AI techniques in rheumatology. AI excels at solving complex data made available by advances in high throughput biological techniques which are increasingly used in rheumatology research (Figure.1). This chapter will showcase some of key ML applications in rheumatology, emphasizing how ML can speed up the study of ARDs towards the goal of personalised medicine. Major challenges and limitations of applying ML in clinical study will also be discussed.
| Type: | Book chapter |
|---|---|
| Title: | AI in Rheumatology |
| ISBN-13: | 9780443156885 |
| Publisher version: | https://shop.elsevier.com/books/artificial-intelli... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inflammation UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156901 |
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