Overview of the NTCIR-18 HIDDEN-RAD Task: Hidden Causality Inclusion in Radiology Report Generation

Abstract

The Hidden-Rad task, introduced as a pilot challenge at NTCIR18, aims to improve the interpretability of AI systems in radiologyrelated diagnostic reasoning by encouraging models to explicitly explain the rationale behind clinical interpretations. Traditional radiology reports often focus on final diagnoses while omitting the underlying causal reasoning. To address this, Hidden-Rad defines two subtasks: Task 1 targets diagnostic explanation generation using radiology reports, with optional use of X-ray images; Task 2 evaluates the interpretation of diagnostic reasoning from structured clinical questionnaires. The task is built on an enriched subset of the MIMIC-CXR dataset and includes formal evaluation criteria provided via a public repository. In total, three teams submitted 40 runs for Task 1, while two teams submitted 16 runs for Task 2. The top-performing systems achieved 69% and 78.84% for each subtask, respectively, demonstrating the potential for integrating causal reasoning into clinical report generation. The findings highlight future directions for explainable medical AI through the use of domain-specific knowledge graphs and customized language models.

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