Artificial intelligence and machine learning in pain research: a data scientometric analysis. (3rd November 2022)

• Record Type: Journal Article
• Authors: Lötsch, Jörn; Ultsch, Alfred; Mayer, Benjamin; Kringel, Dario
• Is Part Of: Pain reports. Volume 7:Number 6(2022)
• Page Start: e1044
• Publication Date: 2022-11-03
• Subjects: Data science, Machine learning, Biometrics, Knowledge discovery, Pain, Precision medicine
• DOI: 10.1097/PR9.0000000000001044
• Languages: English
• ISSNs: 2471-2531

Abstract

Abstract : Machine learning applications are rapidly increasing in pain research and are applied to patient data, while they seem to be little used in preclinical research. Abstract: The collection of increasing amounts of data in health care has become relevant for pain therapy and research. This poses problems for analyses with classical approaches, which is why artificial intelligence (AI) and machine learning (ML) methods are being included into pain research. The current literature on AI and ML in the context of pain research was automatically searched and manually curated. Common machine learning methods and pain settings covered were evaluated. Further focus was on the origin of the publication and technical details, such as the included sample sizes of the studies analyzed with ML. Machine learning was identified in 475 publications from 18 countries, with 79% of the studies published since 2019. Most addressed pain conditions included low back pain, musculoskeletal disorders, osteoarthritis, neuropathic pain, and inflammatory pain. Most used ML algorithms included random forests and support vector machines; however, deep learning was used when medical images were involved in the diagnosis of painful conditions. Cohort sizes ranged from 11 to 2, 164, 872, with a mode at n = 100; however, deep learning required larger data sets often only available from medical images. Artificial intelligence and ML, in particular, are increasingly being applied to pain-related data. This report presents application examples and highlights advantages and limitations, such as the ability to process complex data, sometimes, but not always, at the cost of big data requirements or black-box decisions.