Evaluation of accuracy and potential harm of ChatGPT in medical nutrition therapy - a case-based approach

Abstract

Background ChatGPT is a conversational large language model (LLM) based on artificial intelligence (AI). LLMs may be applied in health care education, research, and practice if relevant valid concerns are proactively addressed. The current study aimed to investigate ChatGPT’s ability to generate accurate and comprehensive responses to nutritional queries created by nutritionists/dieticians. Methods An in-depth case study approach was used to accomplish the research objectives. Functional testing was performed, creating test cases based on the functional requirement of the software application. ChatGPT responses were evaluated and analyzed using various scenarios requiring medical nutritional therapy, which were created with varied complexity. Based on the accuracy of the generated data, which were evaluated by a registered nutritionist, a potential harm score for the responses from Chat GPT was used as evaluation. Results Eight case scenarios with varied complexity when evaluated revealed that, as the complexity of the scenario increased, it led to an increase in the risk potential. Although the accuracy of the generated response does not change much with the complexity of the case scenarios, the study suggests that ChatGPT should be avoided for generating responses for complex medical nutritional conditions or scenarios. Conclusions The need for an initiative that engages all stakeholders involved in healthcare education, research, and practice is urgently needed to set up guidelines for the responsible use of ChatGPT by healthcare educators, researchers, and practitioners. The findings of the study are useful for healthcare professionals and health technology regulators.