Small Language Models for Chronic Diseases: Managing Diabetic Retinopathy in the Global South

Abstract

Access to reliable chronic disease knowledge remains uneven in settings with limited specialist availability, computing resources, connectivity, and funding for developing artificial intelligence (AI) systems. This Paper presents a frugal and responsible knowledge-support framework using diabetic retinopathy (DR) as a use-case domain. The framework converts a curated medical research collection into cleaned passages, clinical entities, knowledge graph (KG) relationships, and compact semantic embeddings stored in a local SQLite database. User questions first pass through scope and safety screening before semantic, keyword, and KG retrieval assemble relevant evidence. Responses can then be produced through a local template or a small language model (SLM), with sources and an education-only boundary retained. The shared knowledge base of our Proof-of-Concept (PoC) contained 160 papers, 6,431 embedded passages, 16,484 entity records, and 6,080 relationships. The same architecture was adapted to two complementary scenarios (prevention counselling and treatment evidence access). These adaptations were achieved mainly by changing the medical research corpus, terminology, relationships, and question sets rather than by training separate large models. The PoC demonstrates the feasibility of building an inspectable, locally stored, and optional clinical support system using modest infrastructure. It does not provide diagnosis, treatment selection, or evidence of clinical effectiveness. Further work would strengthen evaluation, multilingual support, urgent-care routing, evidence updating, and local generative small-model integration.