Abstract
With the advent of machine learning (ML) applications in daily life, the
questions about liability, trust, and interpretability of their outputs
are raising, especially for healthcare applications. The black-box
nature of ML models is a roadblock for clinical utilization. Therefore,
to gain the trust of clinicians and patients, researchers need to
provide explanations of how and why the model is making a specific
decision. With the promise of enhancing the trust and transparency of
black-box models, researchers are in the phase of maturing the field of
eXplainable ML (XML). In this paper, we provide a comprehensive review
of explainable and interpretable ML techniques implemented for providing
the reasons behind their decisions for various healthcare applications.
Along with highlighting various security, safety, and robustness
challenges that hinder the trustworthiness of ML we also discussed the
ethical issues of healthcare ML and describe how explainable and
trustworthy ML can resolve these ethical problems. Finally, we elaborate
on the limitations of existing approaches and highlight various open
research problems that require further development.