7 citations · 9 across the 3 of their papers we have counts for
3 papers
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…
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…