9 citations · 21 across the 6 of their papers we have counts for
6 papers
Enabling Scalable Evaluation of Bias Patterns in Medical LLMs
Hamed Fayyaz, Raphael Poulain, Rahmatollah Beheshti
Large language models (LLMs) have shown impressive potential in helping with numerous medical challenges. Deploying LLMs in high-stakes applications such as medicine, however, brin…
Aligning (Medical) LLMs for (Counterfactual) Fairness
Raphael Poulain, Hamed Fayyaz, Rahmatollah Beheshti
Large Language Models (LLMs) have emerged as promising solutions for a variety of medical and clinical decision support applications. However, LLMs are often subject to different t…
Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks
Mirza Farhan Bin Tarek, Raphael Poulain, Rahmatollah Beheshti
Among various aspects of ensuring the responsible design of AI tools for healthcare applications, addressing fairness concerns has been a key focus area. Specifically, given the wi…
Bias patterns in the application of LLMs for clinical decision support: A comprehensive study
Raphael Poulain, Hamed Fayyaz, Rahmatollah Beheshti
Large Language Models (LLMs) have emerged as powerful candidates to inform clinical decision-making processes. While these models play an increasingly prominent role in shaping the…
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods
Raphael Poulain, Mirza Farhan Bin Tarek, Rahmatollah Beheshti
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare. However, health AI models' overall predi…
An Extensive Data Processing Pipeline for MIMIC-IV
Mehak Gupta, Brennan Gallamoza, Nicolas Cutrona +3
An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes. This growing area of re…