Integrating AI in Healthcare: Methodological Innovations and Real - World Applications
Abstract
This study addresses challenges in AI healthcare application—small medical data samples, imbalanced data, and insufficient interdisciplinary methods. It develops GAN-based data augmentation for small samples and hybrid sampling with weighted loss for imbalanced data. An interdisciplinary method integrating medical statistics, computer science, and public health is proposed. Two case studies verify efficacy: a hospital AI diagnostic system (85% accuracy, 10% higher than manual) and a regional AI platform optimizing resource allocation. Results show the new algorithms (75% accuracy for small samples, 0.8 F1-score for imbalanced data) and interdisciplinary method work, supporting medical AI development.Keywords
Artificial Intelligence; Healthcare; Algorithm Optimization; Interdisciplinary Research; Data Augmentation; AI-Assisted Diagnosis; Regional Healthcare Platform