In the future, AI will routinely recommend treatment options and predict patient outcomes. NLP will make unstructured data accessible, so we expect a reverse of the current trend for structured data entry.

Today mainstream applications of AI include prediction and stratification of rising risk patients. The future of AI will move closer to the diagnoses and treatment of patients which requires FDA approval that has only just started.

The market is developing rapidly with several high-profile start-ups, i.e. Flatiron and Sentrian. New software and services will bring innovative solutions to complex issues in fields such as oncology.

AI will play a bigger role in advancing practices in academic and biopharma by mining rich scientific data sets ultimately, accelerating the pace of basic research and curing diseases.

With mobile sensors and trackers the data is finally there to enable AI to create personalized insights. Digital health delivers it at the right time and place for users.

AI is well suited to address chronic inefficiencies in health markets, potentially lowering costs by the billions, reducing physician burden, creating new tools for diagnosis and treatment, and transforming R&D.

Built on reliable data, healthcare organizations will use Artificial Intelligence/Machine Learning platforms to gain greater insights into patients’ needs to reduce readmission, improve adherence, and adopt new pay-for-performance models.

As with all technologies, healthcare’s adoption of AI has been slow. But, coupled with open and standardised APIs, AI will transform all facets of healthcare and research within 5 years.

AI in healthcare to date has mostly been a matter of experimentation, rather than large scale implementation – but we’re beginning to see real products rolled out, especially in mental health.