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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Mitigating Bias in Radiology Machine Learning: 2. Model Development.

Kuan Zhang¹, Bardia Khosravi¹, Sanaz Vahdati¹

¹Radiology Informatics Laboratory, Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905.

Radiology. Artificial Intelligence

|October 7, 2022

Summary

This summary is machine-generated.

Concerns about artificial intelligence (AI) bias are growing in clinical practice. This report details how bias can occur during AI model development, focusing on data augmentation, model functions, optimizers, and transfer learning in radiology.

Keywords:

Bias Deep Learning Machine Learning Model Radiology

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Area of Science:

Medical Imaging and Artificial Intelligence
Radiology AI Development

Background:

Growing concerns regarding bias and fairness in clinical artificial intelligence (AI) applications.
Model development is a critical stage in implementing machine learning (ML) tools, susceptible to various biases.

Purpose of the Study:

To identify and address potential biases in AI model development within radiology.
To highlight key areas in the model development pipeline where bias can be introduced.

Main Methods:

Focus on four critical aspects of AI model development: data augmentation, model and loss function selection, optimizer choice, and transfer learning.
Review of current practices and potential pitfalls in these development stages.

Main Results:

Bias can be introduced through data augmentation techniques.
Model architecture, loss functions, optimizers, and transfer learning strategies significantly impact AI model fairness.
Specific considerations are needed for each stage to prevent bias.

Conclusions:

Implementing AI in radiology requires careful attention to model development to ensure fairness.
Adopting appropriate practices during data augmentation, model design, optimization, and transfer learning can mitigate bias.
Proactive bias mitigation is essential for trustworthy AI in clinical radiology.