Explainable, trustworthy, and ethical machine learning for healthcare: A survey

With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadblock to clinical utilization. Therefore, to gain the trust of clinicians and patients, we need to provide explanations about the decisions of models. 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 provided a comprehensive review of explainable and interpretable ML techniques for various healthcare applications. Along with…

• QF
  Qatar Foundation
• FN
  Fonds National de la Recherche Luxembourg
• QN
  Qatar National Research Fund

  Award: 13S-0206-200273
• QN
  Qatar National Library