On 18 September, the 4th Annual Public sector transformation Conference organised by Forum Europe and Hewlett Packard Enterprise took place. One of several organized panels addressed Artificial Intelligence (AI) in Healthcare.
The panellists pointed out that AI is an area of strategic importance for Europe and that AI-based solutions can speed up the digital transformation of health and care.
Without making any announcement, the Commission listed the measures that have already been taken to support AI solutions in healthcare through research, shared data infrastructure and legislative frameworks. In particular, the European Union is currently funding AI-based research projects related to modelling and prediction of disease and in-silico clinical trials and decision support systems for diagnostics and treatments
Big data and health
Overall, all the speakers claimed that cooperation for data sharing must be fostered in the European Union and even globally.
The challenge to create the conditions ensuring the correct use of AI-based databases, and guarantee consistency, quality and intelligibly has been stressed.
The key challenges identified by the Commission are as follows:
Inform/Educate about AI
The panel was of the view that there is a need to inform citizens about the potential of artificial intelligence to mitigate bias and increase trust.
A White Paper on speeding drug discovery with AI and Big data published by Hewlett Packard Enterprise was also shared with the audience. The key elements of this White Paper are as follows:
Current processes for drug discovery are time consuming and expensive;
A combination of cutting-edge technologies will define the future of drug discovery;
Greater hardware power will result in the ability to model further drug advances before moving to clinical trials;
Researchers are hoping to someday use computers to simulate the functioning of the human cell, down to subatomic level in order to perform simulations of how different biological compounds work in the human body which will aim to reduce time and cost required to identify drugs for clinical trials.