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Recent advances in machine learning, specifically deep learning, are poised to transform imaging focused healthcare domains including radiology and ophthalmology. But Artificial Intelligence (AI) in medical imaging can be a double-edged sword. Using examples from radiology, oncology, and ophthalmology, we will consider many of the challenges and pitfalls including silent failure of models, lack of repeatability and reproducibility, brittleness, model aging, and a lack of explainability. Models have the potential to entrench and propagate biases. Achieving fairness for all populations is continual challenge. Following a discussion of bias, ethics, fairness and access to equitable healthcare, we will consider some strategies for mitigation including methods for uncertainty estimation and explainability, continuous learning, federated learning, and adversarial training. We will discuss the use of Bayesian deep learning, conformal sets, uncertainty estimation and other technical approaches for increasing model performance, improving fairness and reducing bias. We will consider the ethical dilemmas as we balance increasing access to healthcare through the use of AI, with the challenges of minimizing harm to vulnerable populations. We will also review how medical imaging AI can be used as a lens to study populations and healthcare systems.
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