The article on explainable AI is among the most cited works in the field
The scientific article *A Review of Explainable Artificial Intelligence in Healthcare*, authored by Assoc. Prof. RNDr. Hossein Moosaei, Ph.D., from the Department of Computer Science at the Faculty of Science, UJEP, has garnered significant international attention—it has been listed among the so-called Highly Cited Papers and Hot Papers, i.e., among the most cited and influential works in the field of computer science.
Explainable Artificial Intelligence (XAI) refers to approaches that enable people to understand the decision-making processes of artificial intelligence systems. Unlike “black box” models, XAI helps reveal how and why a system arrived at a specific prediction, thereby significantly increasing the transparency and trustworthiness of these technologies.
This approach is particularly crucial in healthcare, where AI decisions can directly impact patient health. Doctors need not only to accept AI recommendations but also to understand and critically evaluate them—to ensure safety, minimize bias, and support informed decision-making.
The article itself provides a comprehensive overview of XAI methods used in healthcare and highlights their key role in building trust and reliability.
The main message is that the successful deployment of artificial intelligence in clinical practice requires combining high technical accuracy with understandable, human-centered explanations.
You can find the full article HERE.
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