RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis
In the realm of healthcare, particularly neurology, the application of artificial intelligence has become increasingly pivotal. However, the inherent complexity of electronic medical records (EMRs) poses challenges for effective clinical diagnosis using large language models (LLMs). A new framework, RE-MCDF, aims to address these challenges by introducing a closed-loop multi-expert reasoning system designed to enhance the accuracy and reliability of clinical diagnoses.
Challenges in Current Clinical Diagnosis Systems
Current systems utilizing LLMs in clinical settings face several significant hurdles:
- Heterogeneity of EMRs: EMRs are often inconsistent, containing a mixture of structured and unstructured data that complicates analysis.
- Sparsity and Noise: Incomplete or inaccurate data can lead to misinterpretations, ultimately affecting diagnosis outcomes.
- Self-Reinforcing Errors: Single-agent systems may perpetuate errors due to the absence of independent validation, leading to spurious conclusions.
- Shallow Collaborative Reasoning: Existing multi-agent frameworks often lack depth in their collaborative processes, failing to mimic the rigorous analytical methods employed by clinical experts.
- Neglect of Logical Dependencies: Current approaches frequently overlook the intricate relationships among diseases, which can hinder the ability to rule out implausible diagnoses.
The RE-MCDF Framework
To tackle these limitations, RE-MCDF introduces an innovative relation-enhanced multi-expert framework that operates through a generation–verification–revision closed-loop architecture. This framework consists of three key components:
- Primary Expert: Responsible for generating candidate diagnoses along with supporting evidence based on the EMRs.
- Laboratory Expert: Dynamically prioritizes various clinical indicators to ensure that the most relevant data is utilized in the diagnostic process.
- Multi-Relation Awareness and Evaluation Expert Group: Enforces inter-disease logical constraints, ensuring that candidate diagnoses maintain logical consistency and are clinically plausible.
By leveraging a medical knowledge graph (MKG), the first two experts adaptively reweight EMR evidence. The expert group then validates and corrects the proposed diagnoses, creating a feedback loop that strengthens the reliability of the outcomes.
Performance and Implications
Extensive experiments conducted on both the neurology subset of CMEMR (NEEMRs) and a curated dataset (XMEMRs) reveal that RE-MCDF consistently outperforms state-of-the-art baselines in complex diagnostic scenarios. This advancement demonstrates the framework’s potential to significantly improve diagnostic accuracy and reliability in clinical settings.
As healthcare continues to evolve with the integration of AI technologies, frameworks like RE-MCDF are essential for ensuring that LLMs can effectively handle the complexities of EMRs. By fostering deeper reasoning and validation processes, RE-MCDF not only enhances clinical diagnosis but also sets a new standard for the future of AI in healthcare.
For more information and access to the framework, visit the project repository at RE-MCDF on GitHub.
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