AI in Healthcare Virtual Summit Session Recordings

Availability

On-Demand

Expires on Dec 31, 2025

Cost

Member: $120.00

Non-Member: $160.00

In-Training Member: $110.00

Resident Member: $110.00

Resident Non-Member: $160.00

Credit Offered

No Credit Offered

Course Description
Faculty

The Endocrine Society’s AI in Healthcare Virtual Summit 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.

Participants 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. Major content areas include Diagnosis and Prediction, Drug Discovery and Development, and Natural Language Processing (NLP).

Our lineup of talented faculty represent a broad spectrum in AI and healthcare, bringing experience in AI research, device development, and applications to several key endocrine conditions.

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

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.

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.

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.

Evan D. Muse, MD, PhD, FACC, FAHA
Associate Clinical Professor and Associate Program Director
MCTI Scripps Research Translational Institute
Keynote Topic: The Impact of AI in Transforming Care in Cardiometabolic Disease

Maria-Christina Zennaro, MD, PhD
Inserm, Université Paris Cité, Paris Cardiovascular Research Center-PARCC
Topic: Endocrinology and Hypertension

Edward Sazonov, PhD
University of Alabama,
Computer Laboratory of Ambient and Wearable Systems
Topic: Ambient and Wearable Devices, Biomedical Signal Processing, and Health Monitoring

Yao Qin, PhD
Assistant Professor, UC Santa Barbara
Co-Director, REAL AI Initiative
Senior Research Scientist, Google Deep Mind
Topic: AI for Diabetes Treatment, T1D Patients, Nutrition Estimation, and Glucose Control

Wuraola Oyewusi
Data Scientist and AI Technical Instructor
LinkedIn Learning
Topic: AI for Healthcare in Action: What You Need to Know as a Decision Maker?

Nikita Pozdeyev, MD, PhD
Assistant Professor, Biomedical Informatics
University of Colorado, Anchutz School of Medicine
Topic: Artificial Intelligence and Statistical Genetics for Diagnosing Thyroid Cancer

Christopher White, MBBS, PhD, FRACP
Endocrinologist at Prince of Wales Hospital
Randwick, Australia
Topic: Mining the Medical Record for Bone Health

Jeffrey Moon, MD MPH
Assistant Chief Medical Information Officer,
University of Pennsylvania
Topic: Gen AI in Clinical Practice: Promise, But Much to Improve


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