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Asynchronous digital communication with patients has become a routine component of modern healthcare delivery. The rapid growth of patient portals, chatbots, and digital front-door tools has created opportunities for more responsive care, while also increasing communication workload for clinical teams. This systematic review examined artificial intelligence applications in digital patient communication from 2017 to 2026. The review focused on portal message triage, chatbot support, care navigation, automated response drafting, and patient engagement analytics. A PRISMA 2020-compliant review was conducted using structured searches of PubMed, Scopus, IEEE Xplore, and Web of Science. Records were screened by two reviewers, with data extracted on communication domain, AI approach, clinical setting, evaluation strategy, safety reporting, and implementation maturity. The literature was dominated by studies of chatbot support and portal message triage, with a growing body of work on large language model-enabled response drafting. Care navigation and patient engagement analytics were less frequently evaluated, and most studies emphasized technical performance, user satisfaction, or feasibility rather than health outcomes or workload reduction in real-world settings. AI for patient communication appears technically promising in isolated tasks, particularly message classification, chatbot interaction, and draft response generation. However, evidence remains limited regarding safe, equitable, and effective deployment across integrated communication workflows.