Using AI and ML to Diagnose and Treat Rare Diseases There are two major initiatives which currently play a significant role in addressing rare disease challenges, and each relies heavily on AI technologies. Research-Focused Initiative The first is a research-centered initiative started by the NIH, called Rare Diseases Clinical Research Network (RDCRN). With the aim to advance medical research on rare diseases, it provides support for clinical studies and facilitates collaboration, study enrollment, and data sharing. It is designed to promote highly collaborative, multisite, patient-centric, translational and clinical research. Through this network, scientists from various disciplines at multiple clinical sites from all over the world can work with patients and advocacy groups to study over 280 rare diseases.

While innovation can be categorized in familiar terms -- such as a new product, service, or business model -- we're finding companies are applying analytics and artificial intelligence techniques to the concept of innovation. Organizations we work with are turning to tools such as machine learning to pursue a fast, effective route to innovative results as they seek to stay competitive and top-of-mind for consumers. For our Accenture Technology Vision 2016 report, we surveyed 3,100 business and IT executives in 11 countries. We found that 70% of respondents are making significantly more investments in artificial intelligence technologies than they did in 2013, with 55% stating that they plan on using machine learning and embedded AI solutions extensively. An analytics-driven approach to innovation involves a blend of technological experimentation and cultural collaboration -- bringing together fresh minds and analytics techniques to uncover insights and ideas which can inspire agility, industry disruption and customer loyalty.