  Artificial intelligence (AI) and deep learning techniques have gained
significant attraction in recent years, owing to their remarkable capability of
achieving high performance across a broad range of applications. However, a
crucial challenge in training such models is the acquisition of vast amounts of
data, which is often limited in fields like healthcare. In this domain, medical
data is typically scattered across various sources such as hospitals, clinics,
and wearable devices. The aggregated data collected from multiple sources in
the healthcare domain is sufficient for training advanced deep learning models.
However, these sources are frequently hesitant to share such data due to
privacy considerations. To address this challenge, researchers have proposed
the integration of blockchain and federated learning to develop a system that
facilitates the secure sharing of medical records. This work provides a
succinct review of the current state of the art in the use of blockchain and
federated learning in the decentralized healthcare domain.
