Semantic Enrichment of XAI Explanations for Healthcare
| Title | Semantic Enrichment of XAI Explanations for Healthcare |
| Publication Type | Conference Proceedings |
| Year of Publication | 2022 |
| Authors | Corbucci, L, Monreale, A, Panigutti, C, Natilli, M, Smiraglio, S, Pedreschi, D |
| Conference Name | 24th International Conference on Artificial Intelligence |
| Abstract | Explaining black-box models decisions is crucial to increase doctors' trust in AI-based clinical decision support systems. However, current eXplainable Artificial Intelligence (XAI) techniques usually provide explanations that are not easily understandable by experts outside of AI. Furthermore, most of the them produce explanations that consider only the input features of the algorithm. However, broader information about the clinical context of a patient is usually available even if not processed by the AI-based clinical decision support system for its decision. Enriching the explanations with relevant clinical information concerning the health status of a patient would increase the ability of human experts to assess the reliability of the AI decision. Therefore, in this paper we present a methodology that aims to enable clinical reasoning by semantically enriching AI explanations. Starting from a medical AI explanation based only on the input features provided to the algorithm, our methodology leverages medical ontologies and NLP embedding techniques to link relevant information present in the patient's clinical notes to the original explanation. We validate our methodology with two experiments involving a human expert. Our results highlight promising performance in correctly identifying relevant information about the diseases of the patients, in particular about the associated morphology. This suggests that the presented methodology could be a first step toward developing a natural language explanation of AI decision support systems. |