Künstliche Intelligenz in der Plastischen Chirurgie: Postoperative Dokumentation durch Large Language Models

Le résumé fourni par la source

Background: Language-based artificial intelligence (AI) models offer novel opportunities for optimising clinical workflows. One promising application lies in the automation of postoperative documentation in hand and plastic surgery - specifically, procedural coding and the formulation of postoperative care plans. This study aimed to evaluate the performance of AI models in generating postoperative documentation for hand surgery and plastic surgery procedures. Methods: Four standardised operative reports representing common plastic surgical interventions were submitted to ChatGPT o3. The model was prompted to generate procedural codes and to propose appropriate postoperative care recommendations. Coding output was evaluated for accuracy and completeness, while postoperative plans were assessed by three board-certified plastic surgeons using predefined criteria - correctness, completeness, and overall quality - on a 10-point Likert scale (1=very poor, 10=excellent). The time to task completion was recorded in seconds. Results: The AI model achieved a mean coding accuracy of 92.86±14.29% and a completeness score of 90.28±11.45%. Postoperative care recommendations received mean ratings of 7.33±2.10 for completeness, 8.66±0.98 for correctness, and 7.83±1.53 for overall quality. The mean time required for procedural coding was 143.75±46.61 seconds, while postoperative planning required 24.25±11.35. Conclusion: AI models demonstrate promising results in automating postoperative documentation within the field of hand and plastic surgery. Their high coding accuracy, clinically relevant recommendations, and rapid processing make them particularly effective for standardised procedures. Nevertheless, expert review remains essential.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.