​The working group is co-chaired by: Ann Aerts (Novartis Foundation) and Paul Mitchell (Corporate Affairs Microsoft). 

Digital and data can transform health systems

Data and digital technology offer health systems an unprecedented ​opportunity to transform and redesign from being reactive to become preventative and predictive, and enable them to deliver coordinated care and prevention throughout the life journey of the people they serve. To achieve that, digital technology must become an integral part of every health system, because it is as essential as hospital beds are.

The availability of massive quantities of data and clo​ud computing, now enable the use of Artificial Intelligence throughout all aspects of our lives, including health. The use of machine learning and natural language processing can make health systems more efficient and potentially overcome several of their weaknesses, including those related to the shortage of health professionals. ​

The Working Group on Data, Digital, and AI in Health ​is the 3^rd work-stream of the Commission around the theme of digital health, proposed with the vision of generating knowledge on successes, challenges, and learnings from AI solutions in health. The group aims to raise awareness about how data can be transformative for our health systems by proposing practical recommendations and case studies on AI in global health for use and further validation. This can support health professionals and engineers who are working together to solve the most pressing health priorities in different contexts.

Stakeholder groups that can be interested in this work are policy makers, regulators, academics, clinicians, patients, civil society, private companies, entrepreneurs, donors, NGOs or others who want to further explore the potential of AI in health and learn from insights on its development and implementation.

The report will, in principle, focus on LMICs but may bring forth case studies and examples from high-income countries (HICs). We certainly expect that innovative approaches from LMICs can be of interest and applicable to HICs. 

​Membership

WG comprises the following members: (Pending)​