Evidence map›Paper›PMID 37644958›Full record

ArticleDEN open2024

Cost-effectiveness analysis of the artificial intelligence diagnosis support system for early gastric cancers.

Shion Yonazu, Tsuyoshi Ozawa, Tamiji Nakanishi, Kentaro Ochiai, Junichi Shibata, Hiroyuki Osawa, Toshiaki Hirasawa, Yusuke Kato, Hisao Tajiri, Tomohiro Tada

Open access · goldAbstract read
In one paragraph

Article in DEN open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
6.2field-weighted citation impact, top 3% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.

  1. Pooled it
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  4. World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 1 country.

Shion YonazuFaculty of Medicine The University of Tokyo Tokyo Japan.
Tsuyoshi OzawaAI Medical Service Inc. Tokyo Japan.ORCID https://orcid.org/0000-0003-0055-6829
Tamiji NakanishiAI Medical Service Inc. Tokyo Japan.
Kentaro OchiaiAI Medical Service Inc. Tokyo Japan.
Junichi ShibataAI Medical Service Inc. Tokyo Japan.
Hiroyuki OsawaDepartments of Medicine and Gastroenterology Division of Gastroenterology, Jichi Medical University Tochigi Japan.
Toshiaki HirasawaDepartment of Gastroenterology Cancer Institute Hospital of the Japanese Foundation for Cancer Research Tokyo Japan.
Yusuke KatoAI Medical Service Inc. Tokyo Japan.
Hisao TajiriDepartment of Innovative Interventional Endoscopy Research The Jikei University School of Medicine Tokyo Japan.
Tomohiro TadaAI Medical Service Inc. Tokyo Japan.ORCID https://orcid.org/0000-0001-7221-8314
The University of Tokyo · JPJapanese Foundation For Cancer Research · JPJichi Medical University · JPJikei University School of Medicine · JPUniversity of Tokyo Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The introduction of artificial intelligence into the medical field has improved the diagnostic capabilities of physicians. However, few studies have analyzed the economic impact of employing artificial intelligence technologies in the clinical environment. This study evaluated the cost-effectiveness of a computer-assisted diagnosis (CADx) system designed to support clinicians in differentiating early gastric cancers from non-cancerous lesions in Japan, where the universal health insurance system was introduced. Methods: The target population to be used for the CADx was estimated as those with moderate to severe gastritis caused by Results: Compared with the status quo as represented in the base-case analysis, the incremental cost-effectiveness ratio of CADx in the Japanese market was forecasted to be 11,093 USD per quality-adjusted life year. The sensitivity analyses demonstrated that the expected incremental cost-effectiveness ratios were within the willingness-to-pay threshold of 50,000 USD per quality-adjusted life year when the cost of the CAD was less than 104 USD. Conclusions: Using CADx for EGCs may decrease their misdiagnosis, contributing to improved cost-effectiveness in Japan.

Indexed as

artificial intelligencecomputer‐aided diagnosiscost‐effectivenessearly gastric cancermedical cost

Identifiers

PMID37644958
PMCPMC10461711
OpenAlexW4386216023

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.