Title Alternative Better diagnostic performance using computer-assisted diagnostic support systems in internal medicine
Author Kuriyama, Yutaka| Sota , Yumi| Yano, Aika| Yasuda, Hideki| Ishii, Osamu| Saio , Takeo| Torigoe, Keijiro| Ueda, Takeshi | Shimizu , Tarou | Tokuda, Yasuharu|
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)
Publication Title Journal of Okayama Medical Association
Published Date 2019-04-01
Volume volume131
Issue issue1
Start Page 29
End Page 34
ISSN 0030-1558
Related Url isVersionOf https://doi.org/10.4044/joma.131.29
language 日本語
Copyright Holders Copyright (c) 2019 岡山医学会
File Version publisher
DOI 10.4044/joma.131.29
NAID 130007642655