Impact of LLM-supported patient education on patient perspectives and patient-reported outcomes: a mixed-methods systematic review

Large language models (LLMs) are increasingly being explored for patient education, but evidence from patient-facing clinical studies involving real patients remains limited. We conducted a mixed-methods systematic review of LLM-supported patient education in clinical settings, focusing on patterns of use, patient perspectives, and patient-reported outcomes. Searches of PubMed, Embase, Scopus, Web of Science, and CINAHL identified 48 eligible studies from 17 countries. LLMs were mainly used to answer patient questions, generate educational materials, and simplify medical information. Patients generally valued LLM-supported education for clarity, accessibility, and usefulness, but raised concerns about accuracy, privacy, personalization, trust, and emotional support. Quantitative findings were most consistent for cognitive and information-related outcomes, whereas evidence for satisfaction, trust, empathy, preference, behavioral outcomes, and longer-term effects was mixed or limited. Current evidence supports the use of LLMs as adjuncts to patient education, particularly for improving access to and understanding of medical information, while clinician–patient communication remains essential for personalized explanation, trust building, and shared decision-making.