Modeling clinical uncertainty in radiology reports : from explicit uncertainty markers to implicit reasoning pathways
- Author
- Paloma Rabaey (UGent) , Jong Hak Moon, Jung-Oh Lee, Min Gwan Kim, Hangyul Yoon, Thomas Demeester (UGent) and Edward Choi
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- Project
- Abstract
- Radiology reports are invaluable for clinical decision-making and hold great potential for automated analysis when structured into machine-readable formats. These reports often contain uncertainty, which we categorize into two distinct types: (i) Explicit uncertainty reflects doubt about the presence or absence of findings, conveyed through hedging phrases. These vary in meaning depending on the context, making rule-based systems insufficient to quantify the level of uncertainty for specific findings; (ii) Implicit uncertainty arises when radiologists omit parts of their reasoning, recording only key findings or diagnoses. Here, it is often unclear whether omitted findings are truly absent or simply unmentioned for brevity. We address these challenges with a two-part framework. We quantify explicit uncertainty by creating an expert-validated, LLM-based reference ranking of common hedging phrases, and mapping each finding to a probability value based on this reference. In addition, we model implicit uncertainty through an expansion framework that systematically adds characteristic sub-findings derived from expert-defined diagnostic pathways for 14 common diagnoses. Using these methods, we release Lunguage++, an expanded, uncertainty-aware version of the Lunguage benchmark of fine-grained structured radiology reports. This enriched resource enables uncertainty-aware image classification, faithful diagnostic reasoning, and new investigations into the clinical impact of diagnostic uncertainty.
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Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01KWXWYZM33V6MRSQZ5Q4S1GAQ
- MLA
- Rabaey, Paloma, et al. “Modeling Clinical Uncertainty in Radiology Reports : From Explicit Uncertainty Markers to Implicit Reasoning Pathways.” Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), edited by Stelios Piperidis et al., European Language Resources Association (ELRA), 2026, pp. 11852–73, doi:10.63317/2i8nrgcnb52p.
- APA
- Rabaey, P., Moon, J. H., Lee, J.-O., Kim, M. G., Yoon, H., Demeester, T., & Choi, E. (2026). Modeling clinical uncertainty in radiology reports : from explicit uncertainty markers to implicit reasoning pathways. In S. Piperidis, N. Bel, H. van den Heuvel, N. Ide, S. Krek, & A. Toral (Eds.), Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026) (pp. 11852–11873). https://doi.org/10.63317/2i8nrgcnb52p
- Chicago author-date
- Rabaey, Paloma, Jong Hak Moon, Jung-Oh Lee, Min Gwan Kim, Hangyul Yoon, Thomas Demeester, and Edward Choi. 2026. “Modeling Clinical Uncertainty in Radiology Reports : From Explicit Uncertainty Markers to Implicit Reasoning Pathways.” In Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), edited by Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, and Antonio Toral, 11852–73. European Language Resources Association (ELRA). https://doi.org/10.63317/2i8nrgcnb52p.
- Chicago author-date (all authors)
- Rabaey, Paloma, Jong Hak Moon, Jung-Oh Lee, Min Gwan Kim, Hangyul Yoon, Thomas Demeester, and Edward Choi. 2026. “Modeling Clinical Uncertainty in Radiology Reports : From Explicit Uncertainty Markers to Implicit Reasoning Pathways.” In Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), ed by. Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, and Antonio Toral, 11852–11873. European Language Resources Association (ELRA). doi:10.63317/2i8nrgcnb52p.
- Vancouver
- 1.Rabaey P, Moon JH, Lee J-O, Kim MG, Yoon H, Demeester T, et al. Modeling clinical uncertainty in radiology reports : from explicit uncertainty markers to implicit reasoning pathways. In: Piperidis S, Bel N, van den Heuvel H, Ide N, Krek S, Toral A, editors. Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026). European Language Resources Association (ELRA); 2026. p. 11852–73.
- IEEE
- [1]P. Rabaey et al., “Modeling clinical uncertainty in radiology reports : from explicit uncertainty markers to implicit reasoning pathways,” in Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), Palma de Mallorca, Spain, 2026, pp. 11852–11873.
@inproceedings{01KWXWYZM33V6MRSQZ5Q4S1GAQ,
abstract = {{Radiology reports are invaluable for clinical decision-making and hold great potential for automated analysis when structured into machine-readable formats. These reports often contain uncertainty, which we categorize into two distinct types: (i) Explicit uncertainty reflects doubt about the presence or absence of findings, conveyed through hedging phrases. These vary in meaning depending on the context, making rule-based systems insufficient to quantify the level of uncertainty for specific findings; (ii) Implicit uncertainty arises when radiologists omit parts of their reasoning, recording only key findings or diagnoses. Here, it is often unclear whether omitted findings are truly absent or simply unmentioned for brevity. We address these challenges with a two-part framework. We quantify explicit uncertainty by creating an expert-validated, LLM-based reference ranking of common hedging phrases, and mapping each finding to a probability value based on this reference. In addition, we model implicit uncertainty through an expansion framework that systematically adds characteristic sub-findings derived from expert-defined diagnostic pathways for 14 common diagnoses. Using these methods, we release Lunguage++, an expanded, uncertainty-aware version of the Lunguage benchmark of fine-grained structured radiology reports. This enriched resource enables uncertainty-aware image classification, faithful diagnostic reasoning, and new investigations into the clinical impact of diagnostic uncertainty.}},
author = {{Rabaey, Paloma and Moon, Jong Hak and Lee, Jung-Oh and Kim, Min Gwan and Yoon, Hangyul and Demeester, Thomas and Choi, Edward}},
booktitle = {{Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)}},
editor = {{Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio}},
isbn = {{9782493814494}},
issn = {{2522-2686}},
language = {{eng}},
location = {{Palma de Mallorca, Spain}},
pages = {{11852--11873}},
publisher = {{European Language Resources Association (ELRA)}},
title = {{Modeling clinical uncertainty in radiology reports : from explicit uncertainty markers to implicit reasoning pathways}},
url = {{http://doi.org/10.63317/2i8nrgcnb52p}},
year = {{2026}},
}
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