Evidence map›Paper›PMID 39301343›Full record

ArticleCureus2024

Diagnostic Performance of GPT-4o and Claude 3 Opus in Determining Causes of Death From Medical Histories and Postmortem CT Findings.

Masanori Ishida, Wataru Gonoi, Keisuke Nyunoya, Hiroyuki Abe, Go Shirota, Naomasa Okimoto, Kotaro Fujimoto, Mariko Kurokawa, Motoki Nakai, Kazuhiro Saito and 2 more

Abstract read
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Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Masanori IshidaDepartment of Radiology, Tokyo Medical University Hospital, Tokyo, JPN.
Wataru GonoiDepartment of Radiology, The University of Tokyo Hospital, Tokyo, JPN.
Keisuke NyunoyaDepartment of Radiology, The University of Tokyo Hospital, Tokyo, JPN.
Hiroyuki AbeDepartment of Pathology, The University of Tokyo Hospital, Tokyo, JPN.
Go ShirotaDepartment of Radiology, The University of Tokyo Hospital, Tokyo, JPN.
Naomasa OkimotoDepartment of Radiology, The University of Tokyo Hospital, Tokyo, JPN.
Kotaro FujimotoDepartment of Radiology, The University of Tokyo Hospital, Tokyo, JPN.
Mariko KurokawaDepartment of Radiology, The University of Tokyo Hospital, Tokyo, JPN.
Motoki NakaiDepartment of Radiology, Tokyo Medical University Hospital, Tokyo, JPN.
Kazuhiro SaitoDepartment of Radiology, Tokyo Medical University Hospital, Tokyo, JPN.
Tetsuo UshikuDepartment of Pathology, The University of Tokyo Hospital, Tokyo, JPN.
Osamu AbeDepartment of Radiology, The University of Tokyo Hospital, Tokyo, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis study evaluates the diagnostic performance of the latest large language models (LLMs), GPT-4o (OpenAI, San Francisco, CA, USA) and Claude 3 Opus (Anthropic, San Francisco, CA, USA), in determining causes of death from medical histories and postmortem CT findings.

methodsWe included 100 adult cases whose postmortem CT scans were diagnosable for the causes of death using the gold standard of autopsy results. Their medical histories and postmortem CT findings were compiled, and clinical and imaging diagnoses of both the underlying and immediate causes of death, as well as their personal information, were carefully separated from the database to be shown to the LLMs. Both GPT-4o and Claude 3 Opus generated the top three differential diagnoses for each of the underlying or immediate causes of death based on the following three prompts: 1) medical history only; 2) postmortem CT findings only; and 3) both medical history and postmortem CT findings. The diagnostic performance of the LLMs was compared using McNemar's test.

resultsFor the underlying cause of death, GPT-4o achieved primary diagnostic accuracy rates of 78%, 72%, and 78%, while Claude 3 Opus achieved 72%, 56%, and 75% for prompts 1, 2, and 3, respectively. Including any of the top three differential diagnoses, GPT-4o's accuracy rates were 92%, 90%, and 92%, while Claude 3 Opus's rates were 93%, 69%, and 93% for prompts 1, 2, and 3, respectively. For the immediate cause of death, GPT-4o's primary diagnostic accuracy rates were 55%, 58%, and 62%, while Claude 3 Opus's rates were 60%, 62%, and 63% for prompts 1,2, and 3, respectively. For any of the top three differential diagnoses, GPT-4o's accuracy rates were 88% for prompt 1 and 91% for prompts 2 and 3, whereas Claude 3 Opus's rates were 92% for all three prompts. Significant differences between the models were observed for prompt two in diagnosing the underlying cause of death (p = 0.03 and <0.01 for the primary and top three differential diagnoses, respectively).

conclusionBoth GPT-4o and Claude 3 Opus demonstrated relatively high performance in diagnosing both the underlying and immediate causes of death using medical histories and postmortem CT findings.

Indexed as

artificial intelligencecause of deathclaude 3 opusgpt-4olarge language modelpostmortem computed tomography

Identifiers

PMID39301343
PMCPMC11412385

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.