The healthcare industry has been driving the discovery and development of innovative pharmaceuticals and diagnostics that have helped millions of patients across the globe. However, patients are still facing unmet medical needs and healthcare systems, as they are today, are unsustainable.
The healthcare industry therefore has started to move away from the traditional ‘one size fits all’ approach to deliver more precise, evidence-based, personalised and holistic care that delivers value for all stakeholders.
Increasing volumes of multimodal healthcare data and new types of real-world data from digital technologies in combination with analytics (ML/AI) provide us with the tools needed to optimize clinical research and clinical practice and therefore deliver better treatment by increasing the resolution of our understanding of individual patients and their holistic disease journey. Finally, AI applications can also support public health monitoring, research and, ultimately, decision making, ushering a new era of precision public health.
To unleash the full potential of analytics (ML/AI), a number of aspects has to be taken into consideration, for example data quality & bias, technical approaches to developing ‘explainable AI’, methodologies & implementation of AI systems in drug development, diagnostics, clinical trials, clinical decision support systems as well as their, ethical and privacy constraints. A sense of trust in health-related AI technologies is needed to provide appropriate guarantees for the credibility of the healthcare industry. This means we need answers to the question of what it means to deploy truly trustworthy and transparent AI.
The track “AI & Healthcare” aims at bringing together researchers from academia, public health, start-ups and industry to share experiences and best practices, to jointly discuss the potential of AI in the transformation of healthcare towards a trustworthy system with improved patient and society outcomes.
We are calling for participation on the following topics:
data quality & bias, technical approaches to developing ‘explainable AI’,, methodology development toward understanding patient journeys, standards and ethical & privacy constraints for AI in healthcare, methodologies & implementation of AI systems in diagnostics, clinical trials, real world data analysis, drug discovery, drug development, clinical decision support tools.