Swarm Intelligence-Based Auxiliary Diagnosis System

Urban areas often benefit from modern hospitals, advanced medical equipment, and highly trained healthcare professionals, while rural areas usually suffer from a shortage of experienced medical experts and antiquated facilities. This discrepancy leads to lower-quality treatments, higher misdiagnosis rates, and delayed diagnoses in rural areas. To address this issue, this paper proposes a novel swarm intelligence-based auxiliary medical system to mitigate the imbalance in medical resources and improve the accuracy of lung disease diagnosis. Quantitative and qualitative analyses are conducted in this study. Data is gathered through questionnaires given to patients and healthcare providers. The proposed system integrates AI-driven diagnostics with the expertise of real doctors to enhance treatment outcomes. It is structured into four main layers: the front-end layer, back-end layer, database layer, and AI integration layer, each serving distinct functions to achieve the system's objectives. By addressing resource disparities and improving diagnostic accuracy, this innovative medical system offers a comprehensive solution to contemporary healthcare challenges, particularly in underserved regions.