AI
Artificial Intelligence in Medical and Surgical Information: A Systematic Review of Accuracy, Readability, Reliability, and Emotional Resonance
By Francesco Leonforte, Andrea Sebastiano Cavallaro, Vito Nicosia and 11 others.
Abstract
Background
Large Language Models (LLMs) are increasingly utilized for patient-facing healthcare communication. However, their clinical utility remains constrained by communicative, ethical, and safety challenges. This systematic review provides a cross-specialty evaluation of conversational AI, synthesizing evidence on accuracy, readability, informational quality, and emotional resonance.
Methods
Following PRISMA 2020 guidelines and PROSPERO registration (CRD420251021428), PubMed/MEDLINE, Scopus, and Web of Science were searched from 2020 through November 2025. Eligible studies evaluated LLM-generated medical or surgical information against healthcare professionals, validated benchmarks, or alternative AI platforms. Assessed outcomes included factual accuracy, standardized readability indices (e.g., FKGL, FRES), validated quality metrics (e.g., DISCERN, GQS), and emotional or empathetic impact.