Ufuk Burak Karcıoğlu
Healthcare systems worldwide face mounting financial pressures from rising expenditures, claim denials, and administrative inefficiencies within revenue cycle management (RCM). This scoping review examines the role of artificial intelligence (AI) and automation in reducing denial rates and improving financial performance. Following PRISMA-ScR guidelines, PubMed, Google Scholar, and Scopus for English-language publications (2015–March 2026) were searched, supplemented by authoritative reports from WHO, OECD, and leading healthcare organisations. Twenty-four high-quality sources were included, prioritising peer-reviewed articles and rigorous grey literature reporting empirical outcomes.AI modalities predictive analytics, natural language processing, robotic process automation, and emerging agentic systems target front-, mid-, and back-end RCM processes. Real-world implementations, predominantly in US health systems, demonstrate denial rate reductions of 17–35%, first-pass acceptance improvements of 20–30 percentage points, cost-to-collect decreases of 25–60%, and net revenue gains of 2–5%. Broader benefits include shortened accounts receivable days (10–30), reduced staff burnout, and enhanced payer-provider relations. However, evidence remains US-centric, with limited generalisability to diverse global settings, and challenges persist around data quality, algorithmic bias, interoperability, and ethical governance.AI and automation shift RCM from reactive cost centres to proactive strategic assets, enabling resource redirection toward clinical care and equity. Future directions include standardised international benchmarks, rigorous multi-site evaluations, bias mitigation frameworks, and technology transfer to resource-constrained systems. When implemented with multidisciplinary governance and workflow redesign, these technologies may offer a scalable pathway to financially sustainable, resilient healthcare delivery in an era of constrained budgets and rising demand.