The importance of interpretability and visualization in machine learning for applications in medicine and health care

566 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “We will not attempt to cover all the related works many of which are already presented in the research and survey works we cite [1] [2] [9] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23].”
AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, background): “Model complexity not only affects model efficiency but also impacts transparency and interpretability [12] in clinical practice.”
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021, background): “Consideration should be given to the designers of the system, the decision-makers using the systems, and those ultimately impacted by the consequences of those decisions [18,20].”
Explainable artificial intelligence: a comprehensive review (2021, methodology): “In another approach, Wang et al. (2018) implemented the RNNs explainability through the rule extraction from the trained RNNs models.”
Federated learning for medical image analysis: A survey (2023, background): “9) Model Interpretability: In clinical practice, one of the most significant hurdles in adopting machine learning and AI lies in the “black box” nature of certain machine learning systems, such as deep learning [182], [183].”