Artificial intelligence in musculoskeletal conditions

Abstract

Artificial intelligence (AI) is an iterative process by which information is captured, transformed into knowledge and processed to produce adaptive changes in the environment. AI is a broad concept, involving virtual (computing) and physical (robotics) elements. In this narrative literature review, we focus on the aspects of AI that present major opportunities for developing health care. Within a few years, AI will be part of our daily clinical practice. Although significant advances are being made, the application of AI in musculoskeletal medicine is still in its early stages compared with its implementation in other areas of medicine. AI is increasingly being employed in fields such as musculoskeletal radiology, skeletal trauma, orthopedic surgery, physical and rehabilitation medicine and sports medicine, as well as for “big data” and AI in gastrointestinal (GI) endoscopy related injuries. Among the limitations of IA are that it analyzes information based on the data it is supplied, which must therefore be well-labeled and that some algorithms such as DL uses more time, data, and computational power than other techniques. Moreover, AI currently does not solve the problem of causality that exists in medicine with observational data; information that physicians interpret within a broad clinical context. AI should therefore be integrated in a prudent and reasonable manner into the workflows of health professionals.

Transforming medicine: artificial intelligence integration in the peripheral nervous system

“…For instance, AI algorithms have been used to decode neural signals from residual limb muscles in amputees, enabling intuitive and natural control of prosthetic limbs ( 75 ). By analyzing muscle activation patterns, AI can predict the intended movement and execute the corresponding action in the prosthetic limb.…”

Artificial intelligence–assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection

“…The introduction of artificial intelligence (AI) models for diagnosing medical conditions could reduce the burden on healthcare professionals and prevent oversights by alleviating the problem that traditional diagnoses are examiner-dependent [ 17 ]. There has already been success in utilizing AI for endoscopy, chest X-rays, and pathological examinations [ [18] , [19] , [20] , [21] ]. It has been reported that various AI models can facilitate clinical care for professionals, improving diagnosis and treatment for people with hemophilia [ 22 ].…”

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal

“…Various artificial intelligence (AI) algorithms have been employed to evaluate medical images. Machine learning is a subfield of AI that includes models that can learn patterns and improve themselves by making comparisons within the database provided ( 10 , 12 ) .…”

“…In musculoskeletal radiology, a number of studies have investigated the use of AI in tasks such as the diagnosis/classification of fractures, the identification of ligament/meniscal injuries, and the improvement of radiologist workflows ( 12 ) .…”