Literature DB >> 31818381

The Algorithmic Audit: Working with Vendors to Validate Radiology-AI Algorithms-How We Do It.

Vidur Mahajan1, Vasantha Kumar Venugopal2, Murali Murugavel2, Harsh Mahajan2.   

Abstract

There is a plethora of Artificial Intelligence (AI) tools that are being developed around the world aiming at either speeding up or improving the accuracy of radiologists. It is essential for radiologists to work with the developers of such algorithms to determine true clinical utility and risks associated with these algorithms. We present a framework, called an Algorithmic Audit, for working with the developers of such algorithms to test and improve the performance of the algorithms. The framework includes concepts of true independent validation on data that the algorithm has not seen before, curating datasets for such testing, deep examination of false positives and false negatives (to examine implications of such errors) and real-world deployment and testing of algorithms.
Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Keywords:  Accuracy; Artificial Intelligence; Deployment; Testing; Validation

Year:  2020        PMID: 31818381     DOI: 10.1016/j.acra.2019.09.009

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

1.  Radiologist-level Scaphoid Fracture Detection: Next Steps for Clinical Application.

Authors:  Matthew D Li; Martin Torriani
Journal:  Radiol Artif Intell       Date:  2021-06-23

2.  Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) - Changing the Way We Validate Classification Algorithms.

Authors:  Vasantha Kumar Venugopal; Rohit Takhar; Salil Gupta; Vidur Mahajan
Journal:  J Med Syst       Date:  2022-03-05       Impact factor: 4.460

3.  Operationalising AI governance through ethics-based auditing: an industry case study.

Authors:  Jakob Mökander; Luciano Floridi
Journal:  AI Ethics       Date:  2022-05-31

4.  Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.

Authors:  Luke Oakden-Rayner; Jared Dunnmon; Gustavo Carneiro; Christopher Ré
Journal:  Proc ACM Conf Health Inference Learn (2020)       Date:  2020-04

5.  Promises of artificial intelligence in neuroradiology: a systematic technographic review.

Authors:  Allard W Olthof; Peter M A van Ooijen; Mohammad H Rezazade Mehrizi
Journal:  Neuroradiology       Date:  2020-04-22       Impact factor: 2.804

6.  Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs.

Authors:  Nils Hendrix; Ernst Scholten; Bastiaan Vernhout; Stefan Bruijnen; Bas Maresch; Mathijn de Jong; Suzanne Diepstraten; Stijn Bollen; Steven Schalekamp; Maarten de Rooij; Alexander Scholtens; Ward Hendrix; Tijs Samson; Lee-Ling Sharon Ong; Eric Postma; Bram van Ginneken; Matthieu Rutten
Journal:  Radiol Artif Intell       Date:  2021-04-28

7.  Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations.

Authors:  Jakob Mökander; Jessica Morley; Mariarosaria Taddeo; Luciano Floridi
Journal:  Sci Eng Ethics       Date:  2021-07-06       Impact factor: 3.525

  7 in total

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