Thomas Lukasiewicz awarded AXA Chair in Explainable Artificial Intelligence for Healthcare Professorship
Posted: 5th May 2020
Thomas Lukasiewicz is the recent recipient of a prestigious professorship - the AXA Chair in Explainable Artificial Intelligence for Healthcare, which is the first AXA Chair at the University of Oxford. With the generous support of the AXA Research Fund, Professor Lukasiewicz will pursue opportunities to progress the role of AI in improving disease diagnosis, treatment, and prevention in healthcare.
Healthcare is expected to benefit substantially from the recent revolutionary progress in artificial intelligence (AI), because it deals with huge amounts of data on a daily basis, such as patient information, medical histories, diagnostic results, genetic data, hospital billing, and clinical studies. This huge pool of data can train AI to detect patterns and make predictions and recommendations, substantially reducing the uncertainties that professionals face.
The AXA chair will focus on the development of novel AI technologies, called neural-symbolic AI systems, (1) to make more accurate and less expensive diagnoses, (2) to optimise and personalise the treatment of patients, and (3) to prevent diseases, by collecting and using readily available life data from human beings (e.g., via fitness trackers and wearables). This will substantially reduce the costs of healthcare, improve its availability, and reduce mortality and morbidity. A crucial aspect of the technologies to be developed will be the ability (4) to explain the generated outputs: stakeholders in healthcare need to know how a system produces a diagnosis or recommendation.
Furthermore, this explainability will also improve the medical understanding of diseases and their treatments. Besides also contributing to the AI community with novel explainable AI systems, the expected results of this research will thus also contribute to producing new medical insights and progress. The above goals (2) and (3) will also allow for a more accurate risk prediction and the possibility for risk reduction, by choosing a risk-minimising treatment and lifestyle, respectively, within means/resources available.