Researchers are increasingly exploring how to make artificial intelligence more interpretable and useful for real-world health interventions. Shovito Barua Soumma, Asiful Arefeen, and Stephanie M. Carpenter, from Arizona State University, alongside Melanie Hingle (University of Arizona) and Hassan Ghasemzadeh, demonstrate a novel approach using large language models (LLMs) to generate ‘counterfactual explanations’ , essentially, identifying the smallest changes needed to achieve a different outcome from a predictive model. Their work, detailed in a new paper, assesses the performance of models like GPT-4, BioMistral-7B, and LLaMA-3.1-8B, both in standard and fine-tuned configurations, using a clinical dataset , revealing that fine-tuned LLMs, especially…