Beehive Case Studies

Beehive in Diganostic Healthcare

The Beehive method achieved 93% accuracy across 100 medical image classes spanning multiple modalities, including X-ray, CT, MRI, ultrasound, endoscopy, and pathology. These datasets covered a wide range of clinical conditions such as lung diseases (COVID-19, pneumonia, tuberculosis), breast and brain tumours, diabetic foot, melanoma, cervical cancer, and blood cell disorders, representing both organ-level and cellular-level challenges. Beehive operated efficiently on limited computational resources, completing training on a single GPU, and demonstrated exceptional generalisation and stability across diverse datasets. It eliminated catastrophic forgetting by training only on new data with minimal retraining, greatly reducing computation time and energy use. The method also effectively handled imbalanced datasets, maintaining consistent performance across both common and rare diseases. Overall, Beehive proved to be a highly accurate, resource-efficient (≈50% lower computational cost than traditional methods), and sustainable AI system for large-scale medical image analysis across multiple diagnostic domains.