Related papers: A Standardized Radiograph-Agnostic Framework and Platform For Evaluating AI Radiological Systems

A Standardized Radiograph-Agnostic Framework and Platform For Evaluating AI Radiological Systems

URL: http://arxiv.org/abs/2008.07276v1
Date: Mon, 3 Aug 2020 02:09:09 GMT
Title: A Standardized Radiograph-Agnostic Framework and Platform For Evaluating AI Radiological Systems
Authors: Darlington Ahiale Akogo
Abstract summary: We propose a radiograph-agnostic platform and framework that would allow any Artificial Intelligence radiological solution to be assessed on its ability to generalise across diverse geographical location, gender and age groups.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Radiology has been essential to accurately diagnosing diseases and assessing responses to treatment. The challenge however lies in the shortage of radiologists globally. As a response to this, a number of Artificial Intelligence solutions are being developed. The challenge Artificial Intelligence radiological solutions however face is the lack of a benchmarking and evaluation standard, and the difficulties of collecting diverse data to truly assess the ability of such systems to generalise and properly handle edge cases. We are proposing a radiograph-agnostic platform and framework that would allow any Artificial Intelligence radiological solution to be assessed on its ability to generalise across diverse geographical location, gender and age groups.

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