Document Type
Dissertation
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, 3. MEDICAL AND HEALTH SCIENCES
Abstract
The integration of Artificial Intelligence (AI) into healthcare has revolutionized Clinical Decision Support Systems (CDSS) by enabling sophisticated predictive capabilities. However, the opaque nature of many machine learning models, commonly referred to as "black-box" systems, poses significant challenges to their adoption in critical clinical settings where transparency, interpretability, and trust are paramount. This thesis addresses these challenges by developing a rigorous, mathematically grounded framework to evaluate Explainable AI (XAI) methods and enhance their integration into CDSS.
DOI
https://doi.org/10.21427/c41y-j645
Recommended Citation
Aziz Noor, Abdul, "Enhancing Trust in AI for Healthcare: A Quantative Evaluation of Explainable Methods in Clinical Decision Support Systems" (2025). Masters. 116.
https://arrow.tudublin.ie/scienmas/116
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
This dissertation is submitted for the degree of Masters of Philosophy, School of Computer Science, Technological University Dublin, 2025.
doi:10.21427/c41y-j645