Artificial Intelligence and Its Impact on the Quality of Endoscopy Reports

Endoscopy plays a crucial role in reducing the incidence and mortality of gastrointestinal cancers. Ensuring high procedural quality is essential to maximize its effectiveness, and comprehensive endoscopy reports documenting quality-related findings are indispensable. However, generating these reports requires endoscopists to perform numerous manual tasks, from evaluating factors necessary for reporting to documenting findings. Additionally, analyzing endoscopy quality based on reports and related data, such as pathological findings, is labor-intensive. These manual processes are prone to inaccuracies. Artificial intelligence (AI) holds promise for improving the efficiency, accuracy, and quality of endoscopy reporting. AI-driven automation of key evaluation tasks before documentation could significantly reduce the reporting burden on endoscopists while enhancing objectivity and overall report quality. Several AI applications have been explored, including real-time identification and labeling of key anatomical landmarks, examination time assessment, and recognition of endoscopic tools. While full automation of evaluation and documentation using AI remains an ideal yet distant goal, solutions such as voice recognition systems have been developed to alleviate the workload. These systems have demonstrated the potential usefulness in shortening reporting time. Evaluating quality indicators based on endoscopy reports is essential, and monitoring and feedback on these indicators are considered beneficial. Several quality indicators require integration with pathological findings and patient characteristics, which traditionally involves manual data processing. Natural language processing is emerging as a promising alternative to reduce this workload. Further advancements in AI-driven evaluation, documentation, and data integration are needed to fully realize its potential in improving endoscopy report quality.