Active research is being conducted to determine the prognosis for breast cancer. However, the uncertainty is a major obstacle in this domain of medical research. In that context, explanation-aware computing has the potential for providing meaningful interactions between complex medical applications and users, which would ensure a significant reduction of uncertainty and risks. This paper presents an explanation-aware agent, supported by Intensive Knowledge-Distributed Case-Based Reasoning

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... fier (IK-DCBRC), to reduce the uncertainty and risks associated with the diagnosis of breast cancer. A meaningful explanation is generated by inferring from a rule-based system according to the level of abstraction and the reasoning traces. The computer-aided detection is conducted by IK-DCBRC, which is a multi-agent system that applies the case-based reasoning paradigm and a fuzzy similarity function for the automatic prognosis by the class of breast tumors, i.e. malignant or benign, from a pattern of cytological images. A meaningful interaction between the physician and the computer-aided diagnosis system, IK-DCBRC, is achieved via an intelligent agent. The physician can observe the trace of reasoning, terms, justifications, and the strategy to be used to decrease the risks and doubts associated with the automatic diagnosis. The capability of the system we have developed was proven by an example in which conflicts were clarified and transparency was ensured. The explanation agent ensures the transparency of the automatic diagnosis of breast cancer supported by IK-DCBRC, which decreases uncertainty and risks and detects some conflicts.