RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings
This work addresses the time-consuming process of tracking disease progression in radiology for clinicians, but it is incremental as it applies existing LLM methods to a new dataset.
The paper tackled the problem of tracking findings in longitudinal radiology reports by introducing a structured timeline summarization task, where results showed that using group name generation as an intermediate step achieved grouping performance comparable to human annotators with very good recall.
Tracking findings in longitudinal radiology reports is crucial for accurately identifying disease progression, and the time-consuming process would benefit from automatic summarization. This work introduces a structured summarization task, where we frame longitudinal report summarization as a timeline generation task, with dated findings organized in columns and temporally related findings grouped in rows. This structured summarization format enables straightforward comparison of findings across time and facilitates fact-checking against the associated reports. The timeline is generated using a 3-step LLM process of extracting findings, generating group names, and using the names to group the findings. To evaluate such systems, we create RadTimeline, a timeline dataset focused on tracking lung-related radiologic findings in chest-related imaging reports. Experiments on RadTimeline show tradeoffs of different-sized LLMs and prompting strategies. Our results highlight that group name generation as an intermediate step is critical for effective finding grouping. The best configuration has some irrelevant findings but very good recall, and grouping performance is comparable to human annotators.