The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Practice Guidelines (CPGs). For effective use of the CPGs in helping medical professionals in the treatment decision process, it is necessary to fully capture the guideline knowledge, particularly the contexts and their relationships in the graph. While several existing works have utilized these guidelines to create rule bases for Clinical Decision Support Systems, limited work has been done toward directly capturing the full medical knowledge contained in CPGs. This work proposes an approach to create a contextually enriched, faithful digital representation of National Comprehensive Cancer Network (NCCN) Cancer CPGs in the form of graphs using automated extraction and node & relationship classification. We also implement semantic enrichment of the model by using Large Language Models (LLMs) for node classification, achieving an accuracy of 80.86% and 88.47% with zero-shot learning and few-shot learning, respectively. Additionally, we introduce a methodology for answering natural language questions with constraints to guideline text by leveraging LLMs to extract the relevant subgraph from the guideline knowledge base. By generating natural language answers based on subgraph paths and semantic information, we mitigate the risk of incorrect answers and hallucination associated with LLMs, ensuring factual accuracy in medical domain Question Answering.

Deep learning algorithms trained using artificial intelligence (AI) may help to determine how patients will respond to systemic treatments for non-small cell lung cancer (NSCLC), according to new research published in the journal Clinical Cancer Research. For the study, associate research scientist Laurent Dercle (Department of Radiology, Columbia University Irving Medical Center) and colleagues applied AI to standard-of-care (SoC) computed tomography (CT) scans of advanced NSCLC and trained deep learning algorithms to predict how sensitive tumors would be to three types of systemic treatments. Deep learning is a type of machine learning where algorithms called artificial neural networks learn from large datasets and solve problems in a way that mimics how the human brain works and without requiring human supervision. Dercle says that currently, the way radiologists interpret CT scans of patients with cancer who are receiving systemic therapy is essentially subjective. The purpose of this study was to train cutting-edge AI technologies to predict patients' responses to treatment, allowing radiologists to deliver more accurate and reproducible predictions of treatment efficacy at an early stage of the disease," Currently, to check how NSCLC patients' respond to systemic therapies, radiologists assess differences in the size of existing tumors and in the appearance of new tumors that have formed.

Using standard-of-care computed tomography (CT) scans in patients with advanced non-small cell lung cancer (NSCLC), researchers utilized artificial intelligence (AI) to train algorithms to predict tumor sensitivity to three systemic cancer therapies. The study is published in Clinical Cancer Research, a journal of the American Association for Cancer Research, by Laurent Dercle, MD, Ph.D., associate research scientist in the Department of Radiology at the Columbia University Irving Medical Center "Radiologists' interpretation of CT scans of cancer patients treated with systemic therapies is inherently subjective," said Dercle. "The purpose of this study was to train cutting-edge AI technologies to predict patients' responses to treatment, allowing radiologists to deliver more accurate and reproducible predictions of treatment efficacy at an early stage of the disease." To determine if patients with NSCLC are responding to systemic therapy, radiologists currently quantify changes in tumor size and the appearance of new tumor lesions, Dercle explained. However, this type of evaluation can be limited, especially in patients treated with immunotherapy, who can display atypical patterns of response and progression, he noted.