ItemConference Paper

Explainable AI for Diabetic Retinopathy Screening: Enhancing Clinician Trust in Deep Learning Predictions

Title
Explainable AI for Diabetic Retinopathy Screening: Enhancing Clinician Trust in Deep Learning Predictions
Creator
Nibhanupudi, Sarika; Jameer Basha, S.K.; Selvaraj, G.; Gupta, Ranu; Padmarasan, M.; Manikandakumar, M.
Description
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.
Source
CCIC 2026 - Contemporary Computing Innovations Conference 2026;
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Clinical Decision Support; Deep Learning; Diabetic Retinopathy; Explainable AI; Grad-CAM; Medical Imaging
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-831952966-4;
Format
online
Language
English
Type
Conference paper

Citation

Nibhanupudi, Sarika; Jameer Basha, S.K.; Selvaraj, G.; Gupta, Ranu; Padmarasan, M.; Manikandakumar, M., “Explainable AI for Diabetic Retinopathy Screening: Enhancing Clinician Trust in Deep Learning Predictions,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25772.

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