System and method for detecting lung abnormalities in a medical image

Abstract

The present subject matter relates to a system () and a method () for detecting lung abnormalities in a medical image (). The clinical decision support system designed to revolutionize medical diagnosis and treatment recommendations. The system () employs a multi-faceted approach, combining data collection, intelligence-based imaging model (), and language model (), each playing a pivotal role in enhancing clinical decision-making. Data collection forms the foundation, aggregating diverse data sources including chest images, patient history records, EHS, clinical research data and updated healthcare guidelines. This dataset fuels the training and optimization of intelligent models, ensuring their accuracy and relevance. Integration of the intelligent imaging and language model outputs yields an overall confidence score. The system then furnishes a comprehensive output, encompassing diagnosis, confidence score, and tailored treatment recommendations, facilitating informed clinical decision-making. This innovative framework holds promise in augmenting medical practice, offering enhanced diagnostic precision and personalized treatment guidance.