Large language models as sources of patient information on robotic knee arthroplasty: a comparative evaluation.

Comparative quality, accuracy, and readability of large language model responses to patient questions about robotic-assisted total knee arthroplasty.

Although Gemini 3 and DeepSeek demonstrated greater readability in selected comparisons, median responses across all models remained above recommended patient-education reading levels, and LLM-generated responses should be regarded as supplementary rather than standalone sources of patient information regarding RA-TKA.

Evaluating large language models in patient education: a comparative analysis addressing frequently asked questions in peri-acetabular osteotomy.

There may be significant differences in how effectively LLMs support patients with surgical queries, particularly in areas needing detailed explanation, and usually required minimal clarification in areas needing detailed explanation.

Evaluating artificial intelligence-generated clinical guidance for patellar dislocation: accuracy, readability and qualitative appraisal of DeepSeek-R1 responses

Introduction Large language models (LLMs) are increasingly used to answer patient questions, but the readability and information quality of patellar dislocation guidance remain unclear. Methods A series of clinical questions covering etiopathogenesis, mechanisms, clinical manifestations, diagnosis, treatment, complicat...

Evaluation of large language model responses to expert questions in anterior implant dentistry: quality, accuracy, and readability

AI chatbots can generate information with potential clinical relevance in anterior implant dentistry; however, variability in informational reliability persists and expert supervision remains essential before integrating such tools into clinical education.

Generative Artificial Intelligence in Hip and Knee Arthroplasty: A Systematic Review of Emerging Clinical Applications in Patient Communication and Education, Documentation, and Decision Support

LLMs performed well in several aspects of patient communication and education with support from high clinician ratings, accuracy, and readability scores, however, evidence for documentation and decision support remained limited.

Comparison of responses from google and large language models to the top frequently asked questions on lumbar spinal stenosis: an evaluation of accuracy and completeness.