Abstract: 

In the field of healthcare, AI has been applied across the spectrum from diagnostics to prognostics.

Many of these applications have been successfully commercialised yet only some are used in everyday patient care. This talk will introduce the audience to the science behind AI for disease detection (diagnosis) and prediction (prognosis) with a particular focus on musculoskeletal health. We will explore the link between big health data and AI, and finally highlight challenges and opportunities in reliable, representative, scalable and ethical uptake of AI technology in real-world clinical practice.

Learning objectives: Audience will become familiar with the fundamentals of AI applications in healthcare, sources of big health data, branches of AI used in healthcare research and development, and strengths and limitations, highlighting opportunities for budding and experienced data scientists in this rapidly growing and pertinent field.

Bio: 

Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.