Nutritional Language Model (NLM)
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Details and patient eligibility
About
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.
Enrollment
Sex
Ages
Volunteers
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.
Trial design
Primary purpose
Allocation
Interventional model
Masking
6 participants in 1 patient group
Trial contacts and locations
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Data sourced from clinicaltrials.gov
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