Hidden Causality Inclusion in Radiology Reports with Multimodal Small Language Models
Organized by National Institute of Informatics, Tokyo, Japan
Traditional radiology reports document a patient’s symptoms, imaging findings, and final diagnosis, but they rarely make explicit the causal relationships and reasoning that lead to that diagnosis. This omission limits both the interpretability of the report and its value for clinical education. As part of the NTCIR-18 Hidden-RAD Challenge (opens in new tab) (Hidden Causality Inclusion in Radiology Reports), we investigate how AI models can recover this hidden causality — generating a causality-exploration section that reflects the diagnostic reasoning a radiologist implicitly performs.
The work also situates domain-specialized small models against a broad set of general-domain and reasoning-focused baselines, examining how effectively causal reasoning can be integrated with a radiology report or image. Beyond the challenge itself, the recovered causal explanations point toward richer, more transparent automated radiology reporting workflows, where a generated report conveys not only what was observed but why a particular diagnosis follows.