Background: Hand fracture management requires precise diagnostic accuracy and complex decision-making. Advances in artificial intelligence (AI) suggest that large language models (LLMs) may assist or even rival traditional clinical approaches. This study evaluates the effectiveness of ChatGPT-4o, DeepSeek-V3, and Gemini 1.5 in diagnosing and recommending treatment strategies for hand fractures compared to experienced surgeons. Methods: A retrospective analysis of 58 anonymized hand fracture cases was conducted. Clinical details, including fracture site, displacement, and soft-tissue involvement, were provided to the AI models, which generated management plans. Their recommendations were compared to actual surgeon decisions, assessing accuracy, precision, recall, and F1 score. Results: ChatGPT-4o demonstrated the highest accuracy (98.28%) and recall (91.74%), effectively identifying most correct interventions but occasionally proposing extraneous options (precision 58.48%). DeepSeek-V3 showed moderate accuracy (63.79%), with balanced precision (61.17%) and recall (57.89%), sometimes omitting correct treatments. Gemini 1.5 performed poorly (accuracy 18.97%), with low precision and recall, indicating substantial limitations in clinical decision support. Conclusions: AI models can enhance clinical workflows, particularly in radiographic interpretation and triage, but their limitations highlight the irreplaceable role of human expertise in complex hand trauma management. ChatGPT-4o demonstrated promising accuracy but requires refinement. Ethical concerns regarding AI-driven medical decisions, including bias and transparency, must be addressed before widespread clinical implementation.

Objectives: The objective of this study was to assess the T-14 outcomes of the Bizact^TM device for tonsillectomy in a pediatric population. Methods: A case series chart review was undertaken at a public tertiary teaching hospital and private otolaryngology practice, with data collected from pediatric patients who underwent a Bizact^TM tonsillectomy between July 2016 and October 2019 for any indication, whose parents consented to completing the T-14 questionnaire providing a parental perspective of the child’s quality of life. Primary outcomes were T-14 scores recorded preoperatively and 6 weeks post-operatively. Secondary outcome measures were postoperative complications, including hemorrhage and readmission. Results: 146 patients were identified. There was a significant improvement in T-14 scores from a median of 24 (Interquartile range (IQR) 18–33) prior to surgery to 2 (IQR 0–4) at 6 weeks postoperatively (p < 0.001). The post-tonsillectomy hemorrhage rate was 6.1% (9/146 participants). Conclusions: Pediatric Bizact^TM tonsillectomy is effective in treating common indications for pediatric tonsillectomy, reflected by improved parent-reported health-related quality of life T-14 scores postoperatively.