Diagnostic Performance of a Large Language Model for Determining the Cause of Death: A Comparative Analysis of Clinical History, Postmortem Computed Tomography Findings, and Their Integration

Integrating postmortem CT findings with clinical history enhances LLM-based cause-of-death determination accuracy, demonstrating the value of multiple information sources while highlighting opportunities for disease-specific diagnostic optimization.

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Publication Details

Journal
Cureus
Published
2025-05-08
DOI
https://doi.org/10.7759/cureus.83721
Primary Topic
Autopsy Techniques and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
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article

Diagnostic Performance of a Large Language Model for Determining the Cause of Death: A Comparative Analysis of Clinical History, Postmortem Computed Tomography Findings, and Their Integration

Masanori Ishida, Keisuke Nyunoya, Hiroyuki Abé, Tetsuo Ushiku et al.
Cureus
Autopsy Techniques and Outcomes
article

Diagnostic Performance of a Large Language Model for Determining the Cause of Death: A Comparative Analysis of Clinical History, Postmortem Computed Tomography Findings, and Their Integration

Masanori Ishida, Keisuke Nyunoya, Hiroyuki Abé, Tetsuo Ushiku, Osamu Abe, Shohei Inui, Kotaro Fujimoto, Mariko Kurokawa, Naomasa Okimoto, Wataru Gonoi, Kazuhiro Saito, Go Shirota, Akira Katayama, Masumi Takahashi-Mizuki
article en

Abstract

Integrating postmortem CT findings with clinical history enhances LLM-based cause-of-death determination accuracy, demonstrating the value of multiple information sources while highlighting opportunities for disease-specific diagnostic optimization.

CureusVol. 17(5)
University of Tokyo Hospital (JP)
Openalex Percentile: Top 85%
Autopsy Techniques and Outcomes
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Diagnostic Performance of a Large Language Model for Determining the Cause of Death: A Comparative Analysis of Clinical History, Postmortem Computed Tomography Findings, and Their Integration — Masanori Ishida, Keisuke Nyunoya, et al. · Cureus (2025) | TGRS Research Map | TGRS