Disorders of consciousness (DOC), including coma, vegetative state (VS), and minimally conscious state (MCS), pose significant diagnostic challenges in neurology. Recent advances in artificial intelligence (AI), particularly in neuroimaging analysis, offer promising solutions to enhance diagnostic accuracy and improve clinical decision-making in this complex field.

A systematic search was performed in PubMed, Embase, and Scopus for studies using AI tools, like machine learning, to aid in diagnosing disorders of consciousness (DOC). Eligible studies were articles utilizing neuroimaging techniques and reporting diagnostic accuracy metrics. Excluded were non-AI studies, those focused on other neurological conditions, case reports, reviews and books. Reviewers independently screened titles and abstracts, and full-text articles were evaluated for eligibility, with any discrepancies resolved through discussion.

AI-based methods, including machine learning models such as SVM, XGBoost, and DeepDOC, demonstrated high accuracy in diagnosing DOC states. Techniques like EEG microstate analysis, P300 signal detection, and fractal dimension analysis contributed to improved classification of DOC states and prognostic outcomes. AI-enhanced neuroimaging (EEG, fMRI, PET) facilitated the detection of covert consciousness (CMD) in previously undiagnosed patients, and early EEG measures showed potential for predicting long-term recovery outcomes.

AI, particularly machine learning in combination with neuroimaging, holds significant promise for improving diagnostic accuracy and prognostic assessment in DOC patients.While current studies highlight its promise, further research with larger, more diverse cohorts and longer follow-up periods is essential to validate and expand its clinical application.