Blockchain-Based Strategy to Avoid Fake AI in eHealth Scenarios with Reinforcement Learning

Abstract

Every year the healthcare sector suffers from incorrect therapies and an increasing number of patients analysis, which causes congestion in the hospitals and, potentially, worsening of patient's clinical conditions. Extending the concept of the Decision Support System already investigated by the authors, this work advances the state of the art of Reinforcement Learning (RL) via Markov Decision Process formulation, considering an agent acting in his environment motivated by the achievement of the maximum individual objective by appropriate incentives. Transparency, security and privacy of the model are guaranteed by the adoption of Blockchain to enhance the perception of safety around medical operators improving access to hospital services. Experiments focused on the Smart Contract execution time and resources usage have proved the goodness of the proposed model considering both private and public Blockchain configurations.