This project aims to standardize follow-up recommendations for colonoscopies by leveraging Generative AI and Natural Language Processing (NLP) to analyze colonoscopy and pathology reports. Current follow-up guidelines vary based on multiple factors, including polyp type, size, number, and patient history, often leading to inconsistencies in clinical recommendations. The AI system processes unstructured text from medical reports, extracting key diagnostic details and cross-referencing them with established guidelines to generate personalized return date recommendations. By automating this process, the project enhances accuracy, reduces variability in clinical decision-making, and improves workflow efficiency for healthcare providers. The standardized recommendations ensure that patients receive appropriate follow-up care, minimizing the risk of delayed or unnecessary procedures. This initiative demonstrates the potential of AI in streamlining medical decision-making, ultimately contributing to better patient outcomes and more consistent adherence to evidence-based guidelines in gastroenterology.
