Enhancing Fairness in Large Language Models for Clinical Artificial Intelligence Applications Through Fine-Tuning and Prompting

Large Language Models (LLMs) are increasingly being applied to sensitive domains such as medical care, which pose multiple technical and ethical challenges. The most immediate issues include biases built into these models, which are trained on large datasets that may reflect societal prejudices. Such biases can yield outputs that are unfair, ungeneralizable, or even harmful, rendering them clinically inapplicable in the real world and noncompliant with ethical and regulatory constraints. We examine how well bias suppression works when fine-tuned models are prompted across four critical categories (gender, race, profession, and religion). We manually curate a diverse inference dataset and create twelve prompt variants -- six of which are debiased -- to test the outputs of three open-source LLMs: Llama2-7B, Mistral-7B, and Dolly-7B. The fairness and interpretability of the outputs are evaluated using a bias-scoring metric, where lower scores indicate better fairness and interpretability. We also note that debiased prompts reduce bias, and fine-tuning the model performs even better. The results emphasize the critical importance of timely action, model robustness, and ongoing ethical scrutiny for trustworthy and fair LLM deployment in real-world settings, such as medical imaging, and the maintenance of Electronic Health Records (EHR).