Towards Metacognitive Clinical Reasoning: Benchmarking MD-PIE Against State-of-the-Art LLMs in Medical Decision-Making

The ability of large language models (LLMs) to perform clinical reasoning and cognitive tasks within medicine remains a critical measure of their overall capabilities in decision-making, with significant implications for patient outcomes and healthcare efficiency. Current AI models often face limitations in real-world clinical environments, including variability in performance, a lack of domain-specific knowledge, and black-box reasoning processes. In this study, we introduce a novel PIE framework, named MD-PIE, which emulates cognitive and reasoning abilities in medical reasoning and decision-making. We benchmark our framework and baseline methods both quantitatively and qualitatively using state-of-the-art LLMs, comparing them against OpenAIs o1, Gemini 2.0 Flash Thinking, and DeepSeek V3 across diverse benchmarks. Our results demonstrate that MD-PIE surpasses existing models in differential diagnosis and reasoning accuracy across diverse medical benchmarks. This study underscores its potential to improve clinical decision-making through adaptive and collaborative design. Future research should focus on larger benchmarks and real-world validation to confirm its reliability and effectiveness in varied clinical scenarios.