{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10(1)"],"submitter":["Kawamura R"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history-taking system that generates differential diagnosis li"],"journal":["JMIR medical informatics"],"pagination":["e35225"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8832260"],"repository":["biostudies-literature"],"pubmed_title":["Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study."],"pmcid":["PMC8832260"],"pubmed_authors":["Harada Y","Sugimoto S","Shimizu T","Kawamura R","Nagase Y","Katsukura S"],"additional_accession":[]},"is_claimable":false,"name":"Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study.","description":"<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history-taking system that generates differential diagnosis li","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-22T07:48:55.146Z","creation":"2025-04-05T22:16:09.698Z"},"accession":"S-EPMC8832260","cross_references":{"pubmed":["35084347"],"doi":["10.2196/35225"]}}