MultiDx Overall Architecture
Figure 2: The overall architecture of MultiDx.
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Paper title: MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning Abstract: Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While Large Language Models (LLMs) have shown strong capabilities in commonsense reasoning, they still struggle with diagnostic reasoning due to limited domain knowledge. Existing approaches often rely on internal model knowledge or static knowledge bases, resulting in knowledge insufficiency and limited adaptability, which hinder their capacity to perform diagnostic reasoning. Moreover, these methods focus solely on the accuracy of final predictions, overlooking alignment with standard clinical reasoning trajectories. To this end, we propose MultiDx, a two-stage diagnostic reasoning framework that performs differential diagnosis by analyzing evidence collected from multiple knowledge sources. Specifically, it first generates suspected diagnoses and reasoning paths by leveraging knowledge from web search, SOAP-formatted case, and clinical case database. Then it integrates multi-perspective evidence through matching, voting, and differential diagnosis to generate the final prediction.~Extensive experiments on two public benchmarks demonstrate the effectiveness of our approach. Passages referencing this figure: ications Patel et al. ( 2005 ); Lucas et al. ( 2024 ) . It aims to integrate information from clinical case reports (e.g., patient symptoms, test results) to establish a diagnosis (or identify the disease). Beyond achieving the correct diagnostic prediction, it is equally important to ensure that the reasoning process adheres to established medical standards Wu et al. ( 2025 ) . As illustrated in Figure 1 , diagnostic predictions without reasoning can be difficult to verify or justify, potentially undermining trust. In contrast, predictions with structured reasoning not only facilitates patient understanding of their condition but also provides clinicians with verifiable and trustworthy decision support. Achieving interpretable diagnostic predictions forms the foundation of reliable and re reasoning can be difficult to verify or justify, potentially undermining trust. In contrast, predictions with structured reasoning not only facilitates patient understanding of their condition but also provides clinicians with verifiable and trustworthy decision support. Achieving interpretable diagnostic predictions forms the foundation of reliable and responsible AI-assisted healthcare systems. Figure 1: An example of diagnosis reasoning. In recent years, Large Language Models (LLMs) have been widely applied to medical reasoning Tang et al. ( 2025 ); Chen et al. ( 2025 ) . Compared to commonsense reasoning, medical reasoning heavily relies on accurate and comprehensive domain-specific knowledge. Existing approaches incorporate medical knowledge in two main ways. One the one hand, some me
