Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nibhanupudi, Sarika; Jameer Basha, S.K.; Selvaraj, G.; Gupta, Ranu; Padmarasan, M.; Manikandakumar, M. Title A name given to the resource Explainable AI for Diabetic Retinopathy Screening: Enhancing Clinician Trust in Deep Learning Predictions Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived CCIC 2026 - Contemporary Computing Innovations Conference 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CCIC68129.2026.11486007" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CCIC68129.2026.11486007</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038644697?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038644697?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nibhanupudi S., St. Ann's College for women, Department of Mca, Hyderabad, India; Jameer Basha S.K., Gitam (Deemed to be University), Department of Ee and Comm Engg, Hyderabad, India; Selvaraj G., Ramco Institute of Technology, Department of Mathematics, Rajapalayam, India; Gupta R., Jaypee University of Engg and Tech, Department of Ece, Raghogarh, India; Padmarasan M., Panimalar Engineering College, Department of Eee, Chennai, India; Manikandakumar M., Christ University, Bengaluru, India Description An account of the resource Diabetic retinopathy (DR) remains a leading cause of preventable blindness worldwide, with early detection being critical for effective intervention. While deep learning models have demonstrated exceptional performance in automated DR screening, their black box nature has limited clinical adoption due to concerns about interpretability and trust. This paper presents a comprehensive explainable AI (XAI) framework that integrates multiple visualization techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), attention mechanisms, and feature attribution methods, to provide clinically meaningful explanations for DR predictions. We evaluate our approach on the publicly available EyePACS and Messidor-2 datasets, achieving 94.3% accuracy while generating interpretable heatmaps that highlight lesion-specific regions. A clinical validation study involving 15 ophthalmologists demonstrates that our XAI-augmented system increases diagnostic confidence by 23% and reduces review time by 31% compared to non-explainable models. Our findings suggest that transparent AI systems can effectively bridge the gap between algorithmic performance and clinical trust, paving the way for broader adoption of AI-assisted DR screening in healthcare settings. 2026 IEEE. Subject The topic of the resource Clinical Decision Support; Deep Learning; Diabetic Retinopathy; Explainable AI; Grad-CAM; Medical Imaging Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-831952966-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online