NUTRITION IN THE AGE OF AI: CLINICAL CARE, PRACTICAL INNOVATION, AND WHERE THE TOOLS FIT
A client arrives holding a beautifully formatted meal plan generated in a chatbot at 11:00 PM the previous night. It is internally coherent, reads with clinical authority, and aligns perfectly with her aesthetic preferences. Yet, a closer look reveals critical macronutrient calculation errors and hidden nutritional gaps she cannot see.
This scenario defines the current outpatient practice landscape. The challenge for the modern registered dietitian is not simply learning how to access artificial intelligence—it is acquiring the core technical literacy required to critically evaluate its outputs, protect sensitive client data, and confidently handle client conversations when automated tools contradict evidence-based care.
Artificial intelligence will not replace the dietitian, but it fundamentally shifts our professional accountability. This session moves beyond technical hype to deliver a practical framework for integrating AI safely into clinical workflows. We will examine the mechanics of how Large Language Models (LLMs) generate nutrition text based on word probabilities rather than medical truth.
Participants will explore a hands-on, case-driven breakdown of AI tools mapped across Four Functional Quadrants of Practice: from utilizing curated, peer-reviewed clinical AI search engines for point-of-care data mining, to deploying secure ambient clinical AI scribes to conquer charting fatigue. Attendees will learn how to design strict system prompts that train AI to act securely "as-you,"" evaluate real-world privacy risks, and apply practical counseling strategies when a client puts more trust in an algorithm than their care team.
Upon successful completion of this one-hour course, the participant should be able to:
- Explain the technical mechanics of how large language models utilize tokenization, vector embeddings, and next-token prediction to generate persuasive but unverified nutrition content.
- Distinguish generative consumer language models ("public chatbot platforms") from specialized retrieval-augmented evidence-based AI search engines that anchor clinical summaries directly to validated biomedical research to curb automated hallucinations and support evidence-based care.
- Audit an AI-generated clinical output for structural errors, missing nutrient targets, and rigid, weight-centric biases that could compromise client safety or trigger disordered eating behaviors.
- Apply data privacy standards to outpatient workfl ows by identifying the mandatory compliance requirements (such as Business Associate Agreements) needed before utilizing ambient dictation and clinical recording tools.
- Construct a clinical system prompt using structural guardrails that constrain an AI tool's output to specifi c evidence-based philosophies and tone guidelines.
Live Event Date:
Nov 12, 2026
Performance Indicators/Learning Objectives: 5.1.2, 6.1.2, 6.1.7, 6.1.10, 6.3.1, 6.3.5, 9.1.1
Target Audience: RDs, DTRs, Health Professionals
Total Cost:
1 Site Credit
About the Presenter: Kimberly Evans, MS, RD, LDN
Kimberly Evans, MS, RD, LDN, is a Registered Dietitian with 30+ years of experience in clinical practice, higher education, and functional wellness. She holds a master’s in Health Care Administration and a Higher Education Teaching Certificate from Harvard. Kimberly specializes in women’s health, thyroid and metabolic conditions, autoimmune fatigue, and midlife transitions—using a systems-based lens to help dietitians elevate care through real-world, client-centered strategies.
As founder of the Nourish Radiance Method, she offers CEU courses and mentoring that translate lab data and physiology into meaningful, compassionate care. Her teaching bridges evidence-based practice with integrative insight—empowering RDs to lead with clarity, confidence, and impact.
