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Checks and Balances: Unbiased Input and Independent Decision Making in AI Facilitated Adolescent Nutrition Assessment

Checks and Balances: Unbiased Input and Independent Decision Making in AI Facilitated Adolescent Nutrition Assessment
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Checks and Balances: Unbiased Input and Independent Decision Making in AI Facilitated Adolescent Nutrition Assessment

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This interactive session explores the importance and need for conserving independent and critical thinking when using artificial intelligence (AI) in nutrition assessment. Factors including the evolution of AI in clinical care, types of AI available, and their intended strengths and limitations, along with recognizing and addressing the impact of inherent bias on data input into AI tools will be discussed.

Using a case-based approach, the speakers will analyze and compare nutrition assessment findings at two distinct time points in an adolescent case study. Through a structured approach, using synthetic data, learners will critically plan and assess findings at key decision points in the nutrition assessment process to verify the highest level of accuracy, people-centered care, and health equity. Potential shortcomings and solutions for informed and optimal AI application in nutrition care will be examined.

Planned with the Nutrition Informatics DPG

Product Publish Details

Release Date: October 15, 2025

SKU: FNCE25148

CPEU: 1.50 (Valid Until September 16, 2028)

Learning Objectives

  • Analyze the impact of AI on independent and critical thinking in nutrition care decision making.
  • Explain the impact of bias on data input and interpret the evolving challenges in AI for assessing growth and readiness to change, and problem identification.
  • Apply a structured approach utilizing critical thinking to evaluate AI findings in providing accurate people-centered care and minimize bias using an adolescent case study.

Performance Indicators

  • 5.2.1 Identifies potential or existing opportunities and challenges.
  • 10.6.3 Applies counseling principles and evidence-based practice when providing individual or group education.
  • 6.2.1 Integrates technology platforms with other internal and external services (e.g., diet order entry system, risk management, disease management, telehealth, data analytics).

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Christina Biesemeier, MS, RD, LDN, FADA, FAND

Moderator

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Nadine Braunstein, PhD, RD, LDN, FAND

Speaker

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Stephanie Antonelli, MS, RD, LDN

Speaker

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