Trustworthy Artificial Intelligence in Medical Applications: A Mini Survey

Abstract

Nowadays, a large amount of structured and unstructured data is being produced in various fields, creating tremendous opportunities to implement Machine Learning (ML) algorithms for decision-making. Although ML algorithms can outperform human performance in some fields, the black-box inherent characteristics of advanced models can hinder experts from exploiting them in sensitive domains such as medicine. The black-box nature of advanced ML models shadows the transparency of these algorithms, which could hamper their fair and robust performance due to the complexity of the algorithms. Consequently, individuals, organizations, and societies will not be able to achieve the full potential of ML without establishing trust in its development, deployment, and use. The field of eXplainable Artificial Intelligence (XAI) endeavors to solve this problem by providing human-understandable explanations for black-box models as a potential solution to acquire trustworthy AI. However, explainability is one of many requirements to fulfill trustworthy AI, and other prerequisites must also be met. Hence, this survey analyzes the fulfillment of five algorithmic requirements of accuracy, transparency, trust, robustness, and fairness through the lens of the literature in the medical domain. Regarding that medical experts are reluctant to put their judgment aside in favor of a machine, trustworthy AI algorithmic fulfillment could be a way to convince them to use ML. The results show there is still a long way to implement the algorithmic requirements in practice, and scholars need to consider them in future studies.

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