Literature DB >> 33669930

Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.

Yukinori Harada1,2, Shinichi Katsukura2, Ren Kawamura2, Taro Shimizu2.   

Abstract

BACKGROUND: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians' diagnostic accuracy was shown. However, considering the negative effects of AI-driven differential-diagnosis lists such as omission (physicians reject a correct diagnosis suggested by AI) and commission (physicians accept an incorrect diagnosis suggested by AI) errors, the efficacy of AI-driven automated medical-history-taking systems without AI-driven differential-diagnosis lists on physicians' diagnostic accuracy should be evaluated.
OBJECTIVE: The present study was conducted to evaluate the efficacy of AI-driven automated medical-history-taking systems with or without AI-driven differential-diagnosis lists on physicians' diagnostic accuracy.
METHODS: This randomized controlled study was conducted in January 2021 and included 22 physicians working at a university hospital. Participants were required to read 16 clinical vignettes in which the AI-driven medical history of real patients generated up to three differential diagnoses per case. Participants were divided into two groups: with and without an AI-driven differential-diagnosis list.
RESULTS: There was no significant difference in diagnostic accuracy between the two groups (57.4% vs. 56.3%, respectively; p = 0.91). Vignettes that included a correct diagnosis in the AI-generated list showed the greatest positive effect on physicians' diagnostic accuracy (adjusted odds ratio 7.68; 95% CI 4.68-12.58; p < 0.001). In the group with AI-driven differential-diagnosis lists, 15.9% of diagnoses were omission errors and 14.8% were commission errors.
CONCLUSIONS: Physicians' diagnostic accuracy using AI-driven automated medical history did not differ between the groups with and without AI-driven differential-diagnosis lists.

Entities:  

Keywords:  artificial intelligence; automated medical-history-taking system; commission errors; diagnostic accuracy; differential-diagnosis list; omission errors

Year:  2021        PMID： 33669930      PMCID： PMC7924871          DOI： 10.3390/ijerph18042086

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN： 1660-4601            Impact factor:   3.390


  24 in total

1.  Prioritizing Patient Safety Efforts in Office Practice Settings.

Authors:  Steven Kravet; Melissa Bhatnagar; Marian Dwyer; Klaus Kjaer; John Evanko; Hardeep Singh
Journal:  J Patient Saf       Date:  2019-12       Impact factor: 2.844

2.  Understanding Human Over-Reliance On Technology.

Authors:  Matthew Grissinger
Journal:  P T       Date:  2019-06

3.  A survey of outpatient Internal Medicine clinician perceptions of diagnostic error.

Authors:  John C Matulis; Susan N Kok; Eugene C Dankbar; Andrew J Majka
Journal:  Diagnosis (Berl)       Date:  2020-05-26

4.  25-Year summary of US malpractice claims for diagnostic errors 1986-2010: an analysis from the National Practitioner Data Bank.

Authors:  Ali S Saber Tehrani; HeeWon Lee; Simon C Mathews; Andrew Shore; Martin A Makary; Peter J Pronovost; David E Newman-Toker
Journal:  BMJ Qual Saf       Date:  2013-04-22       Impact factor: 7.035

5.  Evaluation of symptom checkers for self diagnosis and triage: audit study.

Authors:  Hannah L Semigran; Jeffrey A Linder; Courtney Gidengil; Ateev Mehrotra
Journal:  BMJ       Date:  2015-07-08

6.  How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs.

Authors:  Stephen Gilbert; Alicia Mehl; Adel Baluch; Caoimhe Cawley; Jean Challiner; Hamish Fraser; Elizabeth Millen; Maryam Montazeri; Jan Multmeier; Fiona Pick; Claudia Richter; Ewelina Türk; Shubhanan Upadhyay; Vishaal Virani; Nicola Vona; Paul Wicks; Claire Novorol
Journal:  BMJ Open       Date:  2020-12-16       Impact factor: 2.692

7.  The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations.

Authors:  Hardeep Singh; Ashley N D Meyer; Eric J Thomas
Journal:  BMJ Qual Saf       Date:  2014-04-17       Impact factor: 7.035
View more
  2 in total

1.  Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study.

Authors:  Ren Kawamura; Yukinori Harada; Shu Sugimoto; Yuichiro Nagase; Shinichi Katsukura; Taro Shimizu
Journal:  JMIR Med Inform       Date:  2022-01-27

2.  Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study.

Authors:  Yukinori Harada; Shinichi Katsukura; Ren Kawamura; Taro Shimizu
Journal:  Int J Environ Res Public Health       Date:  2021-05-23       Impact factor: 3.390
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.