Patient-Perceived and Record-Based Concordance of AI-Generated Perioperative Information After Total Hip Arthroplasty: A Cross-Sectional Study with Multidisciplinary Review

Background and Objectives: Large language models, a form of artificial intelligence (AI), can answer common patient questions about total hip arthroplasty (THA), but most evaluations emphasize clinician-rated accuracy or patient preference rather than whether generalized recovery statements correspond to patients’ documented postoperative courses. We evaluated a fixed set of ChatGPT 5.1 Thinking responses to 12 investigator-selected questions about elective primary THA from patient, medical-record, and multidisciplinary clinical perspectives, with particular attention to complicated recovery. Materials and Methods: In this cross-sectional study with retrospective record review, 101 adults at least 6 months after elective primary THA reviewed a fixed Turkish-language set of ChatGPT 5.1 Thinking answers to 12 investigator-selected questions. Patients rated overall experiential concordance, retrospectively perceived preoperative usefulness, and clarity (0–10). Medical records provided operative duration, first mobilization day, length of stay, and 90-day complications. Three prespecified chart-verifiable timing statements formed a 0–3 timing concordance score. Six multidisciplinary clinicians independently rated all answers during separate 30 min digital assessment sessions.