Search

Patient access to timely, appropriate care remains a persistent challenge for health systems, affecting clinical continuity, patient experience, and operational performance. Artificial intelligence has been proposed as a means of automating and optimizing navigation functions from scheduling to follow-up coordination. This systematic review examined artificial intelligence applications for care navigation and patient access across scheduling optimization, referral management, eligibility screening, digital front-door tools, and follow-up coordination. The review also assessed implementation maturity, evaluation approaches, and equity-related reporting. A PRISMA 2020-compliant search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science for publications from 2017 through 2025. Dual screening, structured data extraction, risk-of-bias assessment, and narrative synthesis were used to characterize the evidence. The literature was concentrated in scheduling optimization, particularly no-show prediction and operational appointment management, and in digital front-door tools such as symptom checkers and triage chatbots. Referral and eligibility applications were emerging, while follow-up coordination models often overlapped with readmission and care-transition prediction. Few studies reported prospective implementation, comparative deployment outcomes, or equity impacts. Artificial intelligence-driven patient access tools appear technically robust for isolated tasks, but evidence that they improve integrated, end-to-end navigation remains limited. The field requires stronger prospective evaluation, equity assessment, and implementation reporting.