Benchmarking Large Language Models on Diagnostic Inference Tasks in Medical Texts

Abstract

The exponential growth of large language models has led to extensive research aimed at evaluating their capabilities for various specialized tasks, particularly in fields where interpretive clarity and diagnostic accuracy are of utmost importance. In medical contexts, the capacity to engage in diagnostic inference relies on multiple interconnected factors, including the ability to parse symptoms, correlate them with potential conditions, and address the nuances of domain-specific language. This paper explores the benchmarking of large language models on diagnostic inference tasks in medical texts, focusing on their performance when tasked with identifying complex disease processes and recommending appropriate clinical interventions. By systematically comparing several leading models, we aim to discern how their learned representations handle synonymy, polysemy, and context-dependent cues critical in medical discourse. Through a robust quantitative approach, our assessment encompasses both standard measures of precision and recall as well as more advanced evaluation metrics that capture the interpretive subtlety required by clinical practitioners. Furthermore, we present analytical perspectives centered on logical consistencies, semantic transparency, and cross-domain adaptability, evaluating the ability of these models to generalize to diverse clinical scenarios. Our results highlight key challenges and emergent strengths in the realm of automated medical reasoning, underscoring potential paths toward advancing large language models to robustly support real-world diagnostic workflows. The findings outlined herein may serve as a foundational basis for future research directed at integrating sophisticated inference mechanisms into medical text processing pipelines.