Over the last decade we have experienced an exponential growth in both the broad interest in & the volume of publication around the theoretical & ethical considerations of using Artificial Intelligence & Machine Learning methodologies within the health sector. What we are seeing is a clear move towards a phase of developmental adoption. The focus is on expanded practical development, with AI/ML based tools being trialled in the support of the key analytical & evidence gathering process stages within HTA /HEOR programmes.

• Evidence screening, gathering & baseline synthesis (aligned with the classic use of SLR-based approaches & descriptive data summary in the HTA process)
• Data analytics & hypothesis testing (providing an approach to deal with large & complex clinical data sets to provide short-list suggestions for patient cohorts & clinical effect groups)
• Predictive data-driven modelling of outcomes

As this pace of development continues, what remains unclear is how can we ensure that, as HTA practitioners & Payer decision makers, we retain common standards of applied rigour & expectations with respect the use of AI/ML in supporting HTA evaluation & decision making.

Our expert panel members will provide their own unique practical perspectives & thoughts around three specific questions:

• Where do you feel we’ll begin to see the first wave of formal inclusion in HTA method guidelines, in terms of the practical applications for AI/ML alongside established HTA / HEOR methodologies?
• What do you see as the primary practical advantages in opening our existing processes to AI/ML enhancement within the context of HTA / HEOR?
• What do you feel are the necessary steps from HTA bodies (such as NICE) to ensure the move to include AI/ML is done in a quality-assured, consistent, ethical, and transparent way?

Sponsored by AXIS Healthcare Consulting Ltd