The reuse of a pre-trained model on a new problem is known as transfer learning in machine learning. A machine uses the knowledge learned from a prior assignment to increase prediction about a new task in transfer learning.The general idea of transfer learning is to use knowledge learned from tasks for which a lot of labeled data is available in settings where only a little labeled data is available.

With an emphasis on engaging and forming collaborative partnerships with healthcare organisations, we develop strategies and applications utilising artificial intelligence to support your organisation via clinical decision support system, medical imaging analysis, and virtual health assistant.

What is Artificial Intelligence (AI) and what are the problems it can solve in healthcare? "[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning …" Machines have far superior computational abilities than humans. They can sort through enormous amounts of data and use it to make better decisions. What are the main components of AI? AI has numerous application areas where it is presenting ground-breaking human level results. All these areas are evolving on daily basis, new research innovations are occurring.

What is the difference between machine learning and deep learning? Machine learning is a subset of Artificial Intelligence that refers to computers learning from data without being explicitly programmed. Deep learning is a subset of machine learning that creates a structure of algorithms to make brain-like decisions. As the name suggests, machine learning is the science of creating algorithms that can learn without being directed by humans. In this context, "learning" emphasizes building algorithms that can ingest data, make sense of it within a domain of expertise, and use that data to make independent decisions.

Precision medicine relies on quick and accurate detection of patients with severe and heterogeneous illnesses. For example, machine learning can be used to identify leukaemia patients based on their blood transcriptomes. However, due to privacy regulations, there is a growing gap between what is permitted and what is technically feasible. Introduction of Swarm Learning-a decentralised machine learning approach combining edge computing, peer-to-peer-networking, and coordination while preserving confidentiality, allows integration of any medical data from anywhere in the world, accelerating the adoption of precision medicine.