• Activity Title

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    Machine Learning, AI, and Health Disparity

     

  • Statement of Need/Program Overview

    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.

  • Educational Objectives

    After completing this activity, the participant should be better 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.

  • Faculty

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

  • Program Agenda

    Front Matter & Registration (5 minutes)
    Educational Presentation (50 minutes)
    Post Test, Evaluation & Certificate delivery (10 minutes)
    Total Activity (65 minutes)

  • Credit Designation - AARC

    This Non-traditional program has been approved for 1.0 contact hour Continuing Respiratory Care Education (CRCE) credit by the American Association for Respiratory Care; 9425 N. MacArthur Boulevard, Suite 100, Irving TX 75063 Course # 193732000

  • Instructions to Receive Credit

    In order to receive credit, participants must complete all of the course lessons. Participants must also score at least a 70% on each chapter quiz. Upon completion of lessons and quizzes, you must also complete the evaluation form. Your certificate will be available to download upon completion of the evaluation.

  • Term Offering-AARC / CRCE

    This activity was released on February 2, 2025 and is valid for two years. Requests for credit must be made no later than February 2, 2027.

    The above term offering is for the following accreditations: AARC.

  • Other Allied Healthcare Professional

    Participation Statement
    Dane Garvin Ltd. will provide a Certificate of Attendance to qualified attendees who successfully complete the enduring activity Machine Learning, AI, and Health Disparity.

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  • Commercial Support Statement

    This activity is supported by an educational grant from Philips Respironics, Inc.

  • Copyright

    Copyright© Dane Garvin Ltd. All Rights Reserved. Permission for use granted to accreditation providers.

  • Web interface provided by Dane Garvin Ltd.

     

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