Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications

Fibromyalgia syndrome (FMS) is a long-lasting medical condition that poses significant challenges for diagnosis and management because of its complex and poorly understood nature. It affects millions of people around the globe, predominantly women, causing widespread pain, fatigue, cognitive impairments, and mood disturbances. The lack of objective measures to address FMS complicates its assessment, often leading to delayed or misdiagnosed cases. By hindering daily activities and productivity, FMS negatively impacts the quality of the patient’s life. Innovative approaches that use medical data, such as bio-signals and bioimaging, combined with machine learning techniques, hold the promise of deepening our knowledge of FMS, which might in turn lead to systems that offer efficient, precise, and personalized physician support. Furthermore, artificial intelligence-driven identification of biomarkers and patient subgroups could improve FMS management. In this systematic review, we explore the role of artificial intelligence in understanding FMS pathophysiology, discuss the present limitations, and shed light on future research avenues, aiming to translate findings into improved clinical outcomes.