Advances in Medical Question Answering and Causal Reasoning

The field of medical question answering and causal reasoning is witnessing significant developments, with a focus on enhancing large language models with external medical knowledge and improving the interpretability and transparency of decision-making processes. Researchers are exploring novel approaches to combine causal-aware document retrieval with structured chain-of-thought prompting, enabling models to retrieve evidence aligned with diagnostic logic and generate step-by-step causal reasoning reflective of real-world clinical practice. Visual analytics is also being investigated as a means to empower people with the tools for sound causal reasoning from health data, with the goal of realizing a new paradigm of data-driven, causality-aware healthcare practices. Noteworthy papers include MedCoT-RAG, which introduces a domain-specific framework for medical question answering, and MIRAGE, which proposes a novel test-time scalable reasoning framework that performs dynamic multi-chain inference over structured medical knowledge graphs. ArgRAG is also notable for its explainable and contestable approach to retrieval augmented generation using quantitative bipolar argumentation.

Sources

MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering

Visual Analytics for Causal Reasoning from Real-World Health Data

MIRAGE: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains

"Where does it hurt?" - Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation

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