Browsing by Author "Jama, Mohamed"

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    Assessing the Current Layout of Artificial Intelligence Applications in Chronic Elbow Pain Disorders: Scoping Review Protocol
    (2024-09-11) Perez, Jose Uriel; Bhogal, Mankirat; Jama, Mohamed; Deng, George
    Review Question: What AI technologies are available for pain medicine physicians treating adult patients with chronic elbow pain? Introduction: Chronic elbow pain, which includes conditions such as tendinopathies, bursitis, nerve compression syndromes, and osteoarthritis, presents a significant diagnostic challenge due to the complexity of the joint and its surrounding structures. Artificial intelligence (AI) holds great potential in improving the efficiency of diagnosis, prognosis, and treatment in musculoskeletal disorders. However, there is no comprehensive review of the AI applications specifically targeting chronic elbow pain. This scoping review seeks to address this gap by analyzing existing AI technologies relevant to pain medicine. Inclusion Criteria: This review will include peer-reviewed studies involving adult patients with chronic elbow pain, where AI technologies are applied to predict, diagnose, manage, or classify these disorders. Studies on AI applications in musculoskeletal and pain medicine that focus on the elbow will be included. Methods: A comprehensive search will be conducted in MEDLINE and Embase databases. Covidence software will be used to manage the selection process, remove duplicates, and facilitate data extraction. The review will follow the PRISMA-ScR guidelines. Findings will be synthesized through a narrative analysis, highlighting the current state of AI applications in chronic elbow pain and identifying research gaps for future investigation. This review will provide a valuable foundation for understanding AI’s role in pain medicine and its potential impact on the management of chronic elbow pain.