Guest Editorial Explainable AI: Towards Fairness, Accountability, Transparency and Trust in Healthcare

The papers in this special section focus on explainable artificial intelligence (AI) healthcare services. Recent advances AI, precision health, and medicine have paved the way for accelerated adaptation use of intelligent tools systems decision-making processes across spectrum. Insights knowledge derived from complex analytics are used to implement diagnostic therapeutic solutions targeted interventions individuals communities globe. Given complexity current multi-dimensional clinical public health data landscape, providing explainability context socio-environmental technical is a key revealing pathways socio-economic disadvantages disparities implementing equitable interventions. As underlying sets AI-based algorithms increases, justifiability insights generated decrease. Humans need understand mechanism behind these know whether they sound, correct, trustable, justifiable make informed decisions. Lack understandability biomedical domain often leads poor transparency accountability ultimately lower quality care suboptimal unfair policies. Explainability considered one prerequisites deep medicine, where AI meant provide composite, panoramic views individuals’ medical data.