Designing a Framework for Explainable Health Recommender System Based on the Ecuadorian Data Protection Regulations

Abstract

Technological advances and development in explainable artificial intelligence (XAI) in the health sector are growing worldwide. However, according to different studies in Latin America, there is a lack of knowledge, know-how, and technical skills to master these new technologies. Most medical software professionals can be considered “black boxes.” This paper focuses on a case study on the lack of technological uses of XAI methods in the health Ecuadorian medical system to support health professionals in treating and managing patients' diseases based on Ecuadorian data protection regulations. A survey was conducted with 71 Ecuadorian medical professionals to know their technological problems in medical appointments. Related works were reviewed to understand techniques or other existing solutions in the XAI field that can complement the designing of so-called health recommender systems. This paper shows the main results of the survey. These results provide guidelines for further designing a framework for managing sensitive data and developing the XAI health recommender system to optimize medical professionals' decision-making, avoiding third-party use of sensitive patient data for other uses.

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