Enhanced medical diagnostic reasoning in small language models using reinforcement learning.

LLMs may help physicians during diagnostic decision making but remain susceptible to cognitive biases or missed diagnostic possibilities. This study examines whether extra training could improve the reasoning of small language models – reinforcement learning with fewer parameters that have been calibrated on a subset of data for a specific use. Using patient records, the researchers improved their models’ diagnostic accuracy—with only a small amount of training data at minimal costs. The results suggest smaller AI models could become affordable tools to support better medical diagnosis.