Module 1: Data and databases in AI and cardiology

For AI innovation, the starting point always has to be the data. But what constitutes good quality data, how can it be accessed, and can you ever have enough?

  • Understanding the different types of data and how to integrate and harmonize datasets
  • Acknowledging the limitations of healthcare data, including heterogeneity
  • Challenges around data privacy and security

James E. Tcheng, MD, FACC, FSACI, Professor of Medicine, Professor of Community Medicine and Family Health (Informatics), Assistant Dean for Academic Appointments, Duke University School of Medicine

Louise Sun, MD SM FRCPC FAHA, Anesthesiologist and Director of Big Data and Informatics Research, University of Ottawa Heart Institute

Dr Ameet Bakhai, MBBS, MD, FRCP, FESC, Research Director, Cardiologist, Heart Function Improvement Lead & Founder Amore Health Ltd



Module 2: Machine and deep learning in cardiology 

Machine and deep learning can help in reviewing data, as well as potentially seeing patterns which clinicians might not otherwise pick up. This session will offer real examples that are happening in cardiology right now.

  • Convolutional neural networks
  • Recurrent neural networks
  • Deep reinforcement learning

David E. Albert, MD, Founder and Chief Medical Officer, AliveCor

Dr Rob Brisk, Clinical Research Fellow, Craigavon Area Hospital



Education partner presentation: Combining feasibility with practicality in AI application development

The feasibility of applying AI to help solve imaging challenges in cardiology is clear. However, the emerging technology will only be adopted if it is practical and flexible enough to add value within the clinical workflow. In this session, IBM will share how the combination of global leadership in AI science and deep experience in cardiology imaging workflow helps to create better adoption of AI in cardiology.

Julie Pekarek, Offering Management Executive, IBM Watson Health



Module 3: Natural language processing and workflow in cardiology

NLP is already being used to great effect across care settings, including patient education, monitoring and coordination of care. This session will feature live examples, as well as the different business and care delivery models that are working in deployment.

  • Applying NLP to EHR and data mining
  • Use of conversational AI in decreasing physician burnout
  • Using virtual assistants, voice and conversation recognition to enhance care at home

Professor Partho Sengupta, Chief, Division of Cardiology, Director, Cardiovascular Imaging; Professor of Medicine, WVU Heart and Vascular Institute

Dr Jai Nahar, Attending, Division of Cardiology, Children’s National Hospital, Associate Professor of Pediatrics, George Washington School of Medicine, Washington, DC.



Module 4: Issues in AI in cardiology

Picking up on some of the prevailing concerns around AI in cardiology, including data governance and liability, regulatory compliance and explainability.

  • Addressing the key ethical questions around artificial intelligence
  • Mitigating bias through true data diversity
  • Regulation and control of data sharing: public interest vs. patient privacy
  • FDA clearance and CE marks

Professor Francisco Lopez-Jimenez, Co-Director, Artificial Intelligence in Cardiovascular Medicine, Mayo Clinic

Thierry Mesana, MD, PhD, President and CEO, University of Ottawa Heart Institute, Emeritus Professor of Surgery, University of Ottawa