Artificial Intelligence in Healthcare Virtual Summit

The Endocrine Society’s AI in Healthcare Virtual Summit, November 8-9, 2024, is an innovative 2-day virtual event designed to inform providers, healthcare professionals, researchers, technologists, industry stakeholders, and educators on the capabilities of artificial intelligence in the healthcare field. This summit offers a unique opportunity to delve into the transformative potential of AI in revolutionizing patient care and shaping the future of medicine. 

Attendees will discover how AI technologies are redefining diagnostics, treatment planning, and patient outcomes in healthcare in addition to exploring the latest advancements in AI-driven healthcare, from predictive analytics to machine learning algorithms. The summit will be held in conjunction with Matchbox Virtual, which provides an innovative user experience that mimics attendance at a physical conference site. Major content areas include Diagnosis and Prediction, Drug Discovery and Development, and Natural Language Processing (NLP).

Keynote Speaker:

Dr. Evan D. Muse, MD, PhD, MCTI Scripps Research Translational Institute, is a preventive cardiologist dedicated to reducing the health burdens associated with heart disease. His mission is to identify risk factors in patients to optimize lifestyle and treatment strategies before the symptoms of heart disease manifest. He is an Associate Clinical Professor of Medicine and Associate Program Director for Research of the Cardiovascular Disease Fellowship at the Scripps Clinic, as well as an Assistant Professor of Molecular Medicine at the Scripps Research Translational Institute in La Jolla, California. As a physician-scientist, Dr. Muse aims to improve patient outcomes through the use of polygenic risk scores and digital medicine approaches, integrating genetics, lifestyle, and health data to optimize medical treatments and lifestyle recommendations. With a passion for cross-disciplinary collaboration, he has served as a judge for the Qualcomm Tricorder XPRIZE and IBM Watson AI XPRIZE competitions, sits on the Founding Members Council of the Digital Medicine Society, and is an Associate Editor for the Nature Partner Journal - Digital Medicine.

• Guiding ultrasound image capture with artificial intelligence
• Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management
• More than meets the eye: Using AI to identify reduced heart function by electrocardiograms

Faculty:

Guillaume Assié, PhD, MD

Service d’Endocrinologie de Cochin
Institut Cochin, Inserm CNRS Université de Paris
Chaire IA en Santé de la fondation UP, Dpt IA en Santé, UFR médecine
Président de ENSAT
Content focus: Bioinformatics and endocrine tumors

Maria-Christina Zennaro, MD, PhD 

Research Professor
Université Paris Cité
Paris Cardiovascular Research Center – PARCC 
Hôpital Européen Georges Pompidou- HEGP 
Content focus: Endocrinology and hypertension

Edward Sazonov, PhD

Cudworth Professor of Engineering
The University of Alabama College of Engineering
Tuscaloosa, Alabama
Head of Computer Laboratory of Ambient and Wearable Systems
Content focus: Ambient and wearable devices, biomedical signal processing, health monitoring

Yao Qin, PhD 

Assistant Professor, UC Santa Barbara
Co-Director, REAL AI initiative
Senior Research Scientist, Google DeepMind
Content focus: AI for diabetes treatment, T1D patients, nutrition estimation, and glucose control

Christopher White, MBBS, PhD, FRACP

Director of the Department of Endocrinology
Prince of Wales Hospital
Randwick, Australia.
Content focus: AI for Bone Health

More faculty to be added soon.

Focus Areas:

Throughout the summit, participants will engage with leading experts and industry pioneers to explore:

1. Diagnosis and Prediction: AI algorithms can analyze medical images, such as X-rays, MRIs, and CT scans, to assist doctors in detecting abnormalities and making accurate diagnoses. AI can also analyze patient data, including symptoms, medical history, and genetic information, to predict the likelihood of certain diseases or conditions.
2. Drug Discovery and Development: AI is being used to accelerate the drug discovery process by analyzing vast amounts of biological and chemical data to identify potential drug candidates. AI algorithms can also predict how different drugs will interact with specific patients, leading to more targeted and effective treatments.
3. Natural Language Processing (NLP): NLP algorithms can analyze unstructured data from sources like electronic health records, medical notes, and research papers to extract valuable insights and support clinical decision-making. NLP-powered chatbots can also interact with patients to answer questions, provide information, and schedule appointments.