Program Goals

The purpose of this educational activity is to provide a primer on common health disparities and challenges to make healthcare more equitable through big data machine learning artificial/augmented intelligence and big data. The presentation will also provide examples of how AI and ML can contribute or exacerbate health disparities. One key challenge is the issue of bias in AI and the presentation will highlight how these biases are engendered and offer solutions to overcoming bias in healthcare-related AI. The presentation will end with future directions to make AI fulfill its promise as a transitional tool, one that effectively translates discovery to treatments and solutions for all. 

Target Audience

Everyone in the healthcare and public health ecosystems (patient provider, payer, industry, and policymakers). Due to cross-cutting nature of the presentation, it is likely that data scientists, engineers, and technologists will find this presentation informative. 

Learning Objectives

After completion of this activity participants will be able to:

1. Identify 3 health disparities in cardiovascular health, cancer, kidney disease, and dermatological disease.
2. Describe how poor assessment, diagnostic and treatment among racial/ethnic minorities lead to health disparities.
3. Discuss how Machine Learning can improve assessment, diagnosis and treatment customization and adherence.
4. Recognize cautionary topics that must be taken into consideration (such as AI bias).
5. Identify emerging topics in Machine Learning and how they can solve additional health disparities.

Speakers

Azizi Seixas, PhD
Associate Professor, Department of Psychiatry and Behavioral Sciences
University of Miami School of Medicine

Partner

Dane Garvin

Accreditation Statement

This activity is provided by MEDtalks

Physician Accreditation Statement - EACCME

MEDtalks is accredited by the European Accreditation Council for Continuing Medical Education (EACCME) to provide the following CME activity for medical specialists.

Physician Credit Designation - EACCME

MEDtalks designates this enduring material for a maximum of 1.0 UEMS Credits™. Each medical specialist should claim only those hours of credit that he/she actually spent in the educational activity. The EACCME is an institution of the European Union of Medical Specialists (UEMS). Only those e-learning materials that are displayed on the UEMS-EACCME website have formally been accredited. Through an agreement between the European Union of Medical Specialists (UEMS) and the American Medical Association (AMA), physicians may convert EACCME credits to an equivalent number of AMA PRA Category 1 CreditsTM. Information on the process to convert EACCME credit to AMA credit can be found at www.ama-assn.org/education/earn-credit-participation-international-activities.