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ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora — arXiv:2608.20369

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.20369
License (as archived): CC0-1.0
Archived: 2026-10-18T10:18:11.435Z
SHA-256 of the archived text: 4a71c68f93abf9e30932d20ad572ca6c57a9f731058c441a4bd06521c6c52577
Stable URL of this page: https://metraipolis.com/documento/dc_751b2602f503

How to cite

ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora — arXiv:2608.20369 — arXiv (en), https://arxiv.org/abs/2608.20369 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_751b2602f503 (Metraipolis archive dc_751b2602f503, sha256 4a71c68f93abf9e30932d20ad572ca6c57a9f731058c441a4bd06521c6c52577)

BibTeX

@misc{dc751b2602f503,
  title = {ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora — arXiv:2608.20369},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.20369},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_751b2602f503; sha256 4a71c68f93abf9e30932d20ad572ca6c57a9f731058c441a4bd06521c6c52577},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.20369. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Structured reporting converts free-text radiology narratives into queryable data keys, facilitating cohort assembly, longitudinal tracking, and training label generation for medical AI. The prevailing paradigm follows a two-stage pipeline: (1) constructing a reporting template, (2) extracting information to populate it. While the extraction stage has benefited from advances in large language models (LLMs), template construction remains a manual bottleneck relying on labor-intensive expert consensus that is static, difficult to scale, and may fail to capture real-world reporting diversity. We address this limitation with \textbf{\texttt{ASTAR}}, an LLM-based framework for Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora. Extensive experiments on 4,215 fetal brain MRI reports from multiple centers demonstrate that the \textbf{\texttt{ASTAR}}-induced template surpasses two expert-curated templates across template coverage, information fidelity, diagnostic fidelity, and expert-rated usability, reducing template development from weeks of committee deliberation to hours of automated processing. Code: this https URL

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-24 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.20369 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T10:17:37.556Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).