Integrating causal inference into artificial medical diagnostic reasoning
Suitable for:
Student, Bachelor or Master projectDescription:
Can we enhance the accuracy and interpretability of artificial medical diagnostic reasoning by integrating causal inference methodologies? This project aims to explore the integration of causal inference techniques into existing AI models for medical diagnosis. By leveraging causal reasoning, we can overcome the limitations of purely correlational analyses and provide clinicians with more reliable diagnostic insights. The project involves developing and integrating causal inference models into AI frameworks, and training and validating the integrated model using rigorous evaluation procedures. The expected outcomes include improved diagnostic accuracy, enhanced interpretability, and the identification of causal factors influencing diseases.