Big data analytics is playing an increasingly important role in healthcare provision and research due in large part to the advent of low-cost tools for data transmission, storage, security, computation and visualization. By providing the tools to assimilate large volumes of disparate, structured, and unstructured data produced by healthcare systems, it is increasingly possible to analyze this data in its entirety to increase speed and accuracy of diagnosis, evaluate therapy, and expand researchers’ ability to conduct disease exploration. The long-term goal of vendors working on the algorithms that turn this data into insight is artificial (or applied) intelligence (AI) within healthcare, where the machine imitates intelligent human behavior and performs tasks indistinguishably from a human counterpart. The first critical step toward true AI is machine learning, where these algorithms, also known as machines, are trained to better identify patterns and iteratively improve successful diagnosis without explicit programming.
The research contained in this report provides a taxonomy of the ecosystem supporting machine learning with diagnostic applications, the use cases for the machines, an estimate of investment in machine development, and the partnerships between suppliers and the healthcare industry. In total, more than 260 companies, academic organizations, and healthcare providers have been researched. The companies and organizations profiled are focused on aggregating and analyzing large datasets of information from clinical care services for the purpose of improving clinical decision support and health outcomes, not business analytics.
Principal Analyst, Medical Technology
Shane Walker focuses primarily on medical devices and healthcare I.T. His current medical technology coverage areas include software and storage related to patient management and care coordination, virtual care provision, and emerging technologies being developed for diagnostic and therapeutic applications.