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