The study topic focuses on creating an AI framework that is human-centric and comprehensible to increase diagnostic precision in healthcare. The goal of the study is to develop an AI system that can provide suggestions that medical practitioners can comprehend and rely on, in addition to making accurate diagnoses and providing transparent justifications for those diagnoses. The research effort aims to improve the interaction between AI and healthcare practitioners, ultimately resulting in enhanced diagnosis accuracy and patient outcomes, by applying human-centric principles and interpretability methodologies.
A human-centric explainable AI framework for improved diagnosis accuracy is a topic that is highly relevant to the healthcare industry. For efficient treatment planning and patient care, a timely and accurate diagnosis is essential. The approach overcomes the black-box characteristic of AI models, which frequently prevents their acceptance in healthcare, by offering accessible explanations for diagnoses produced by AI. Healthcare professionals and patients will benefit from the research's increased trust and confidence in AI systems, which will improve diagnostic precision, lower medical errors, and improve patient outcomes.
Some potential areas in the field of human-centric explainable AI for enhanced diagnostic accuracy in healthcare for further investigation include:
This research will help undergraduate students develop valuable skills, including:
