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Knowledge graphs (KGs) such as , SNOMED‑CT , and disease‑specific ontologies have been employed to enrich AI predictions with clinical context. For imaging, Radiology Ontology –based reasoning has shown promise in reducing false positives (Liu et al., 2021).

, the school carries a legacy of service and excellence. This foundation pushes students to look beyond their textbooks and ask how they can contribute to their communities and the wider world. The DLDSS Spirit Whether it’s in the science lab, on the sports field, or in the middle of a heated debate, the spirit of "DLDSS -121" (a common social tag for the school community) is one of resilience and ambition. The school continues to prove that with the right guidance, students from the Federation can compete on any stage—from local exams to international diplomatic forums. Join the Conversation Are you a DLDSS alum or a current student? We want to hear your favorite memories! Whether it’s a specific teacher who changed your life or a trip that opened your eyes, share your story in the comments below. Would you like to focus this blog post on a specific area, such as dldss -121

Figure 1 depicts the overall pipeline of DLDSS‑121. The system consists of four interconnected modules: Knowledge graphs (KGs) such as , SNOMED‑CT ,

Commercial systems such as , Zebra Medical Vision , and Viz.ai provide AI‑driven alerts but often rely on single‑task models and lack explainability. Academic efforts (e.g., DeepRadiology , Lee et al., 2020) integrate multi‑task learning but typically omit a reasoning layer that can incorporate patient‐specific information. This foundation pushes students to look beyond their