Impact of Large Language Model–Based AI Tools on Physician-Patient Communication: Systematic Review and Meta-Analysis

Background: Recent advances in large language models (LLMs) such as GPT-3/4 have spurred the development of artificial intelligence (AI) chatbots and advisory tools in medicine. These systems are posited to assist or augment physician-patient communication, potentially improving empathy, clarity, and responsiveness. However, their actual impact on communication outcomes remains uncertain. Objective: This study aimed to systematically review and meta-analyze peer-reviewed studies (2020-2025) evaluating how LLM-based interventions affect physician-patient communication, including empathy, clarity, trust, and patient understanding. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we searched PubMed/MEDLINE, Embase, Scopus, and Web of Science for studies published from 2020 to 2025 examining LLM or chatbot applications in clinical communication contexts. Eligible designs included randomized, observational, cross-sectional, and qualitative studies. Two reviewers (WHP and SR) independently screened titles or abstracts, assessed full texts, and extracted data on study design, population, LLM type, communication measures, and outcomes. We conducted a qualitative synthesis and random-effects meta-analysis, reporting pooled standardized mean differences or odds ratios with 95% CIs. Results: From 312 records, 10 studies were included, all quantitative and predominantly cross-sectional. Populations ranged from patients with chronic conditions to health care professionals and laypersons. Outcomes assessed included empathy (8 studies), clarity or information quality (6 studies), satisfaction or usefulness (4 studies), and trust perceptions (2 studies). In 6 direct comparisons of AI- versus physician-generated responses, LLMs were rated significantly higher in empathy in 5 studies. One large study found that chatbot replies were judged empathetic in 45.1% of cases versus 4.6% for physician replies (odds ratio approximately 9.8, P