Literature DB >> 30646173

Evaluating Artificial Intelligence Applications in Clinical Settings.

Elaine O Nsoesie1.   

Abstract

Mesh:

Year:  2018        PMID: 30646173     DOI: 10.1001/jamanetworkopen.2018.2658

Source DB:  PubMed          Journal:  JAMA Netw Open        ISSN: 2574-3805


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  11 in total

1.  Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.

Authors:  Valentina Bellemo; Gilbert Lim; Tyler Hyungtaek Rim; Gavin S W Tan; Carol Y Cheung; SriniVas Sadda; Ming-Guang He; Adnan Tufail; Mong Li Lee; Wynne Hsu; Daniel Shu Wei Ting
Journal:  Curr Diab Rep       Date:  2019-07-31       Impact factor: 4.810

2.  Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians.

Authors:  Avishek Choudhury
Journal:  JMIR Hum Factors       Date:  2022-06-21

3.  Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers.

Authors:  Dong Wook Kim; Hye Young Jang; Kyung Won Kim; Youngbin Shin; Seong Ho Park
Journal:  Korean J Radiol       Date:  2019-03       Impact factor: 3.500

4.  What should medical students know about artificial intelligence in medicine?

Authors:  Seong Ho Park; Kyung-Hyun Do; Sungwon Kim; Joo Hyun Park; Young-Suk Lim
Journal:  J Educ Eval Health Prof       Date:  2019-07-03

5.  Artificial intelligence for ultrasonography: unique opportunities and challenges.

Authors:  Seong Ho Park
Journal:  Ultrasonography       Date:  2020-11-03

6.  Artificial intelligence in breast cancer screening: primary care provider preferences.

Authors:  Nathaniel Hendrix; Brett Hauber; Christoph I Lee; Aasthaa Bansal; David L Veenstra
Journal:  J Am Med Inform Assoc       Date:  2021-06-12       Impact factor: 4.497

7.  Artificial intelligence: opportunities and implications for the health workforce.

Authors:  Indrajit Hazarika
Journal:  Int Health       Date:  2020-07-01       Impact factor: 2.473

8.  Predictive analytics by deep machine learning: A call for next-gen tools to improve health care.

Authors:  Philip Wells
Journal:  Res Pract Thromb Haemost       Date:  2020-01-10

9.  Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.

Authors:  Yan Li; Matthew Sperrin; Darren M Ashcroft; Tjeerd Pieter van Staa
Journal:  BMJ       Date:  2020-11-04

Review 10.  Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence.

Authors:  Seong Ho Park; Jaesoon Choi; Jeong Sik Byeon
Journal:  Korean J Radiol       Date:  2021-03       Impact factor: 3.500

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