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ID 57006
Sort Key
7
Title Alternative
Better diagnostic performance using computer-assisted diagnostic support systems in internal medicine
Author
Kuriyama, Yutaka Okayama University Medical School
Sota , Yumi Okayama University Medical School
Yano, Aika Okayama University Medical School
Yasuda, Hideki Yasuda Clinic of Internal Medicine
Ishii, Osamu Torigoe-iin
Saio , Takeo Health Management Office, SMBC Nikko Securities Inc
Torigoe, Keijiro Torigoe-iin
Ueda, Takeshi Emergency and General Internal Medicine, Rakuwakai Marutamachi Hospital
Shimizu , Tarou Department of Diagnostic and Generalist Medicine, Dokkyo Medical University
Tokuda, Yasuharu Muribushi Okinawa for Teaching Hospitals
Abstract
The recent application of artificial intelligence(AI)to clinical medicine has confirmed the usefulness of AI for diagnostic imaging, histopathological examinations, and dermatologic screening. Clinical decision support systems are another promising area to which AI could contribute toward better clinical decisions. We have developed computer-assisted diagnostic support systems to reduce human diagnostic errors such as delayed diagnoses, misdiagnoses, and overdiagnoses. Our three Diagnosis Reminder(DR)systems include two AI systems that use machine learning in their diagnosis algorithms. Here, we compared the diagnostic accuracy of a DR-supported group with that of an unassisted physicians group, using three difficult patient cases provided by experts in general medicine.  Our analyses revealed that the three AI diagnostic systems could not provide accurate differential diagnoses up to top 10 in all three patient cases because of incomplete data inputs for machine learning. However, the first DR system, which was developed by an experienced diagnostician over the last 35 years, showed very useful performance in reducing human diagnostic errors when it was used by an expert physician. The use of AI diagnostic systems by knowledgeable physicians will lead to better diagnostic performance. We also discuss the current scenario, future challenges, and prospects for AI diagnostic systems herein.
Keywords
AI 診断システム (AI diagnostic systems)
診断思い出し (diagnosis reminder)
機械学習 (machine learning)
診断エラー (human diagnostic errors)
Note
原著 (Original)
Publication Title
Journal of Okayama Medical Association
Published Date
2019-04-01
Volume
volume131
Issue
issue1
Publisher
岡山医学会
Publisher Alternative
Okayama Medical Association
Start Page
29
End Page
34
ISSN
0030-1558
NCID
AN00032489
Content Type
Journal Article
Related Url
isVersionOf https://doi.org/10.4044/joma.131.29
OAI-PMH Set
岡山大学
language
日本語
Copyright Holders
Copyright (c) 2019 岡山医学会
File Version
publisher
Refereed
True
DOI
NAID
Eprints Journal Name
joma