The advent of health technologies associated with artificial intelligence (AI) is deemed a transformative shift in the delivery of medical care within our lifetime. Nevertheless, there is a communication gap between intelligent models and clinical experts. Transitioning towards Human-Centered AI can serve as a means to bridge this gap. This study introduces a human-centered rule extraction model based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II), designed to enhance the interpretability and clinical utility of diagnostic tools in healthcare. This model autonomously generates diagnostic rules, adjusts threshold values for variables, and involves clinical experts in evaluating the rules, thereby ensuring the relevance and applicability of extracted rules in real-world settings. Experiments on the WBC, WDBC, and Pima datasets showed that the proposed model outperformed state-of-the-art rule extraction methods in the literature in terms of predictive value accuracy (PVA) and support. On the subset of data covered by the extracted rules, it achieved accuracy comparable to traditional black-box methods without sacrificing interpretability. The extracted rules were clinically evaluated by 13 domain physicians, with all approved rules achieving a content validity index (CVI) of at least 0.85. Additionally, the model provides multiple high-performance alternative diagnostic rules per class, giving clinicians practical flexibility. Our approach emphasizes the importance of multidisciplinary collaboration between AI specialists and healthcare professionals, aiming to build trust in AI-driven diagnostic systems through transparency and clinical validation.
Ahouz et al. (Tue,) studied this question.