Clinical trial · Interventional
Nutritional Language Model
Comparative Analysis Between Artificial Intelligence vs. Human Generated Nutrition Messages for Colorectal Cancer Survivors
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| No Disease State or Condition | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Nutritional Messaging | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Dietician
- interventionNames
- Other: Nutritional Messaging
Primary outcomes (5)
- measure
- Outcome Measure Title: Readability of Nutrition Messages
- timeFrame
- 8 to 12 months
- description
- Description: The readability of AI-generated and human expert-generated nutrition messages will be measured using the Flesch-Kincaid Grade Level tool. Unit of Measure: Grade level score (numerical score indicating reading difficulty level). Measurement Tool: Flesch-Kincaid Grade Level formula. Scale values: The values vary from 0 to 18, where 18 represents the most difficult text.
- measure
- Outcome Measure Title: Thematic Relevance of Nutrition Messages
- timeFrame
- 8 to 12 months
- description
- Description: Thematic relevance of nutrition messages will be assessed by experts in nutrition using a thematic coding framework specifically designed for this study. Unit of Measure: Percentage (%) of messages that align with pre-determined thematic codes relevant to colorectal cancer survivorship. Measurement Tool: Thematic coding framework created by the research team. Scale values: The themes are capability (C), opportunity (O), and motivation (M) as three key factors capable of changing behavior (B).
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 18+ years of age * Currently practicing Registered Dietitian Nutritionist with at least five years of experience working with oncology patients and survivors in their practice. * Must have access to computer and internet access. Exclusion Criteria: * Non-English speakers, as the study materials and assessments are in English. * Experts with conflicts of interest related to any of the LLMs that are being evaluated.
References
Publications (1)
- BACKGROUNDShah NH, Entwistle D, Pfeffer MA. Creation and Adoption of Large Language Models in Medicine. JAMA. 2023 Sep 5;330(9):866-869. doi: 10.1001/jama.2023.14217. PMID 37548965