Our approach bridges the gap between cutting-edge explainable AI research and real-world healthcare applications.
State-of-the-art explainability techniques adapted for healthcare contexts
SHapley Additive exPlanations for feature importance
Local Interpretable Model-agnostic Explanations
PyTorch model interpretability library
What-if scenario analysis for decision boundaries
Bayesian and ensemble methods for confidence quantification
We leverage publicly available healthcare datasets for research and development
Large-scale chest X-ray dataset with 14 disease labels
Used under NIH Clinical Center licenseCritical care database for clinical research
PhysioNet credentialed access requiredAll datasets are used in compliance with their respective licenses and ethical guidelines. Patient privacy and data protection are paramount in our research activities.
Research publications and real-world case studies from our work
Comprehensive analysis of XAI methods in radiology and pathology
Framework for identifying and mitigating algorithmic bias in clinical settings
Guidelines for creating understandable AI explanations for patients
Our commitment to responsible AI development in healthcare
AI augments, never replaces, clinical decision-making
Proactive identification and mitigation of potential risks
Continuous monitoring for algorithmic bias and fairness
Clear explanations for all stakeholders in the care process
Download our comprehensive technical brief covering methodologies, case studies, and implementation guidelines.