NCT06661590

Nutritional Language Model

Comparative Analysis Between Artificial Intelligence vs. Human Generated Nutrition Messages for Colorectal Cancer Survivors

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 6 (actual)
• Sponsor: University of Minnesota · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

Colorectal cancer survivors often face unique nutritional challenges and require support in their recovery and long0term health. While human experts have traditionally provided that support, there has been an increase in the use of Large Language Models (LLM) in medicine and in nutrition. The LLM offers a potential supplementary resource for generating personalized nutritional advice, specifically in personalized messaging. However, the efficacy and reliability of these AI-generated messages in comparison to human expert advice remain underexplored specific to this population. This study aims to compare the nutrition-related content generated by popular LLMs-ChatGPT, Claude, Gemini, and Co-Pilot-against messages crafted by human experts. By evaluating the generated content in terms of readability, thematic relevance, medical relevance, perceived effectiveness, and implementation of participants' clinical practice, this research will provide insights into the strengths and limitations of using AI for nutritional guidance in colorectal cancer care.

Conditions

• No Disease State or Condition

Interventions

Type	Name	Description
OTHER	Nutritional Messaging	Dieticians will evaluate nutritional messages created by LLM and Human Experts.

Timeline

• Start date: 2024-10-15
• Primary completion: 2024-12-20
• Completion: 2025-02-06
• First posted: 2024-10-28
• Last updated: 2025-05-15

Locations

1 site across 1 country: United States