The severe stage of diabetic retinopathy (DR) is often identified by the presence of hard exudates on ophthalmologic examination. For precisely detecting exudates, Optic Disc (OD) segmentation is significant due to the high similarity between exudates and the OD. The primary objective of this OD-SAM study is to examine the feasibility of a zero-shot framework for OD segmentation, intending to improve subsequent hard exudate detection in retinopathy screening. The proposed work integrates automatic OD localisation using the peak-end thresholding approach, prompt-based optic disc segmentation using the segment anything model (SAM), a multi-criteria decision-making (MCDM) approach for optimal OD mask selection, and ellipse fitting to smooth the disc boundary. Subsequently, the OD is removed to evaluate its impact on YOLO-based hard exudate detection. The efficiency of this proposed framework is tested on the IDRiD dataset. It achieved an 86.2% overlap and a 90.7% dice coefficient. The results show the effectiveness of the proposed approach for OD segmentation and highlight improvements in exudate detection after OD removal. This study can support lesion analysis and grading in an automated diabetic retinopathy screening and severity assessment.