Turing test-inspired method for analysis of biases prevalent in artificial intelligence-based medical imaging.

Abstract

Due to the growing need to provide better global healthcare, computer-based and robotic healthcare equipment that depend on artificial intelligence has seen an increase in development. In order to evaluate artificial intelligence (AI) in computer technology, the Turing test was created. For evaluating the future generation of medical diagnostics and medical robots, it remains an essential qualitative instrument. We propose a novel methodology to assess AI-based healthcare technology that provided verifiable diagnostic accuracy and statistical robustness. In order to run our test, we used a state-of-the-art AI model and compared it to radiologists for checking how generalized the model is and if any biases are prevalent. We achieved results that can evaluate the performance of our chosen model for this study in a clinical setting and we also applied a quantifiable method for evaluating our modified Turing test results using a meta-analytical evaluation framework. His test provides a translational standard for upcoming AI modalities. Our modified Turing test is a notably strong standard to measure the actual performance of the AI model on a variety of edge cases and normal cases and also helps in detecting if the algorithm is biased towards any one type of case. This method extends the flexibility to detect any prevalent biases and also classify the type of bias.