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Clinical documentation is central to continuity of care, coding, billing, compliance, and quality measurement, yet it remains a major source of administrative burden for physicians. Artificial intelligence, especially natural language processing, has been proposed as a way to improve documentation quality, coding accuracy, billing support, and clinical workflow efficiency. This systematic review synthesised evidence from 2017 to 2023 on natural language processing methods applied to clinical documentation improvement. The review focused on automated clinical coding, note quality, billing support, computer-assisted physician documentation, and physician workflow efficiency. A PRISMA 2020-compliant search strategy was applied to PubMed, Scopus, IEEE Xplore, and Web of Science for publications from 1 January 2017 to 31 December 2023. Screening was performed in duplicate, and eligible studies were narratively synthesised by documentation domain, model type, deployment maturity, and evaluation approach. The evidence base expanded rapidly during the review period, especially in automated coding and clinical note generation. Several studies reported technically promising systems for ICD coding, documentation summarisation, and speech-derived notes, whereas billing outcomes and sustained workflow effects were less commonly evaluated. Natural language processing for clinical documentation improvement appears most mature for automated coding and note analysis. Evidence for billing support and physician workflow transformation remains less developed, particularly in prospective clinical environments.