biostudies-literatureUnknown10(1)Kawamura R<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 liJMIR medical informaticse35225https://www.ebi.ac.uk/biostudies/studies/S-EPMC8832260biostudies-literatureIncidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study.PMC8832260Harada YSugimoto SShimizu TKawamura RNagase YKatsukura SfalseIncidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study.<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 li2022-01-01T00:00:00Z2022 Jan2025-04-22T07:48:55.146Z2025-04-05T22:16:09.698ZS-EPMC88322603508434710.2196/35225