Hybrid Vision-Language Models for Real-Time Surgical Report Generation and Documentation

Abstract

The integration of artificial intelligence (AI) in healthcare has significantly improved surgical documentation and workflow efficiency. Traditional manual documentation methods are time-consuming, prone to errors, and can divert surgeons’ attention from critical tasks. This research explores the development of Hybrid Vision-Language Models (VLMs) for real-time surgical report generation and documentation, leveraging state-of-the-art deep learning techniques in computer vision and natural language processing (NLP). Our proposed model integrates a vision module that captures and analyses surgical video frames using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) and a language module based on pre-trained transformer models such as GPT-4 or BERT. A fusion mechanism aligns visual features with textual context, enabling accurate, structured report generation. We employ supervised and contrastive learning techniques to enhance model performance. The system is trained on large-scale, annotated surgical datasets such as Cholec80 and HeiChole. Evaluation metrics include BLEU, ROUGE, METEOR scores, and real-time efficiency analysis. Experimental results indicate higher accuracy and reduced documentation time compared to traditional methods. Challenges such as data scarcity, computational costs, and ethical considerations are discussed, along with future directions in self-supervised learning, edge AI deployment, and explainability. This research aims to revolutionize surgical documentation, reducing cognitive workload for medical professionals while enhancing patient safety and compliance. The proposed AI-driven approach paves the way for real-time, automated, and highly accurate surgical reporting systems that can be seamlessly integrated into modern healthcare environments.