Publications & Research

Chen, L., Martinez, R., et al.

We present a comprehensive multimodal AI system that integrates clinical notes, laboratory results, vital signs, and medical imaging to provide real-time clinical decision support in intensive care units.

Johnson, M., Patel, S., Thompson, K., et al.

This paper introduces a novel framework for explainable AI in medical diagnosis, addressing the critical need for transparency and interpretability in clinical AI systems.

Rodriguez, A., Kim, H., Singh, P., et al.

We demonstrate the effectiveness of federated learning approaches for training medical AI models across multiple institutions while preserving patient privacy and data security.

Liu, X., Brown, T., Wilson, J., et al.

Novel AI algorithms identify previously unknown biomarkers predictive of immunotherapy response, enabling precision oncology approaches for cancer treatment.

Anderson, K., Lee, Y., Davis, R., et al.

Large-scale clinical validation of deep learning models for medical image analysis across multiple imaging modalities and clinical conditions.

Garcia, M., Taylor, S., Williams, C., et al.

Comprehensive evaluation of bias mitigation techniques in healthcare AI systems, with emphasis on algorithmic fairness across diverse patient populations.