Optimizing Causality-Based Radiology Reporting with Retrieval-Augmented and Structured Reasoning Approaches for the NTCIR-18 HIDDEN-RAD Task

The nash team participated in the NTCIR-18 Hidden-RAD Task, focusing on generating causality-based diagnostic inferences from radiology reports. In Subtask 1, we applied a cost-efficient API-driven inference pipeline to recover hidden causalities within MIMIC-CXR reports. Our pipeline integrates few-shot in-context learning, retrieval-enhanced prompting, and strict candidate selection using an evaluation checklist. By leveraging retrieved similar cases to enrich the prompt dynamically, this approach achieved the highest ranking (1st place) in the official evaluation. In Subtask 2, we explored structured diagnostic reasoning using PRISMA-Guided Causal Explanation, applying prompt-based systematic reasoning to enhance interpretability. Our method, leveraging structured PRISMA flow with large language models, secured 2nd place in the official evaluation. Additionally, we investigated an alternative approach that combined fine-tuning and domain-specific prompting to improve model adaptability. While this method was not included in the final ranking, it demonstrated potential in enhancing domain-specific model interpretability. These findings contribute to the advancement of explainable AI (XAI) in radiology, bridging the gap between automated diagnosis and human expert decision-making.