Abstract
Effective patient-physician communication is a cornerstone of surgical care, yet increasing clinical complexity and time constraints often limit opportunities for shared decision-making. Artificial intelligence (AI) is emerging as a tool to supplement these interactions by improving perioperative risk communication, tailoring patient education, and reducing physician workload. Current applications include chatbots for preconsultation history gathering, AI-generated decision aids, and risk prediction models, which synthesize large datasets to provide individualized outcome predictions. Visualization platforms and large language model-driven documents further demonstrate potential to improve readability, completeness, and patient comprehension compared with traditional surgeon-generated materials. Research suggests that AI may enhance shared decision-making by increasing patient understanding, reducing decisional conflict, and supporting individualized risk-benefit discussions. Populations with complex needs-including patients with multiple comorbidities, limited English proficiency, or low health literacy-may experience particular benefit from AI-assisted communication. Indirect advantages are also evident; studies of ambient AI scribes report decreased documentation burden, reduced after-hours work, and improved patient engagement, suggesting downstream improvements in the patient-physician relationship. Despite these promising developments, limitations persist. AI models are prone to bias and lack transparency, raising concerns regarding fairness, accuracy, and trustworthiness. Overreliance on AI risks diminishing essential human elements of care, while errors in translation or misrepresentation of nuance may disproportionately affect vulnerable groups. Ensuring robust evaluation, representative training datasets, and clinician oversight is critical to safe implementation. AI tools show substantial promise in improving surgical communication, but more research is required to establish their effectiveness, address ethical challenges, and define best practices for integration into clinical care.