Artificial Intelligence in Healthcare Part II: The Practical Application – A European Perspective  

Though currently very exciting, Artificial Intelligence (AI) is by no means new to Healthcare. AI applications have been around for several decades undergoing several ‘Hype’ cycles. After AIbased expert systems underperformed in the 1980s, AI returned to accompany the first Genomics wave during the early 2000s, e.g. Bioinformatics routinely applied AI in the analysis of high-dimensional micro-array data. However, as those AI applications were not on the critical path, they remained mostly “under the radar”. So, why the current excitement? The recent awakening of AI in Healthcare is the result of converging developments. For example:

1. AI applications – especially Deep Neural Networks (DNN) – have successfully penetrated everyday consumer products.
2. The availability and relative ease of generating large biomedical data sets enables training of DNNs.
3. Advances in compute power.
4. Investments in the Data Science workforce fueling new thinking and applications of AI in Healthcare;
5. Advanced Analytics becoming recognized as fundamental to tackling R&D efficiency gaps and innovation.

The previous PRISME Forum Technical Meeting (Fall 2017, Cambridge, MA) addressed the topic of ‘The Potential of AI in Healthcare’. This Technical Meeting (Spring 2018, Paris, France) will focus on the practical application of AI in Healthcare and from a European perspective. The expectation is that sharing information about the actual deployment of AI applications will lessen the hype and promote this Technical Meeting as an excellent venue for learning and inspiring. The PRISME Forum had appointed a Technical Meeting Advisory Committee (TMAC) to organize this meeting. TMAC provided guidance both on topics, the agenda and the faculty.

The TMAC saught qualified speakers and contributors to address relevant AI applications in Healthcare including:

1. Current or emerging applications of AI in Healthcare directed to different data types, e.g. Sensor Data, Genetics Data, Chemical Library and Biological Assay Data, Imaging Data; Unstructured Data including Real-World Data and Social Media
2. Examples of the application of AI along the value chain, e.g. Drug and Biomarker Discovery, Lead ID and Optimization, Safety and Pharmacovigilance, Disease Understanding, Clinical Development including Trial Design and Patient Recruitment, and Healthcare applications beyond R&D
3. Challenges and opportunities for AI Applications in Healthcare: Data Volume & Quality (Curation); Domain Complexity, Temporality.
4. European Startups offering novel AI applications or methods.
5. AI-enabled process efficiency gains, including emerging AI application in RPA (Robotic Process Automation)