Publications by authors named "Shu Sugimoto"

Background: Artificial intelligence (AI) symptom checker models should be trained using real-world patient data to improve their diagnostic accuracy. Given that AI-based symptom checkers are currently used in clinical practice, their performance should improve over time. However, longitudinal evaluations of the diagnostic accuracy of these symptom checkers are limited.

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Background: Low diagnostic accuracy is a major concern in automated medical history-taking systems with differential diagnosis (DDx) generators. Extending the concept of collective intelligence to the field of DDx generators such that the accuracy of judgment becomes higher when accepting an integrated diagnosis list from multiple people than when accepting a diagnosis list from a single person may be a possible solution.

Objective: The purpose of this study is to assess whether the combined use of several DDx generators improves the diagnostic accuracy of DDx lists.

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Background: Automated medical history-taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of these systems on diagnostic errors in clinical practice remains unknown.

Objective: This study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)-driven automated medical history-taking system that generates differential diagnosis lists was implemented in clinical practice.

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Upside down stomach (UDS) is a rare type of hiatal hernia. It is categorized by herniation of either the entire stomach or most of the gastric portion of the stomach into the posterior mediastinum. Acute UDS usually presents with abdominal and thoracic symptoms immediately after the stomach rotates, while chronic UDS often has either mild or no symptoms with the entire stomach rotated in the mediastinum.

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