Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2025
Artificial intelligence (AI) in nutrition: A case-based comparison of generative AI models.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Hurt Ryan T, Mundi Manpreet S, Bonnes Sara L, Salonen Bradley R, Muthusamy Kalpana, Stephenson Chris R, Croghan Ivana T, Fokken Shawn C, Edakkanambeth Varayil Jithinraj
Аннотация
BACKGROUND: Clinical nutrition (CN) is becoming increasingly complex because of the rising prevalence of chronic illness, cancer, and malnutrition-related conditions such as short bowel syndrome and refeeding syndrome. Despite its clinical significance, nutrition education among US physicians remains limited. Simultaneously, large language model (LLM)-based artificial intelligence assistants (AIAs) have emerged as tools to support complex clinical decision-making but remain largely untested in CN.
METHODS: This retrospective study evaluated four LLM-based AIAs-ChatGPT (OpenAI), OpenEvidence (OpenEvidence Inc), Gemini (Google, Google DeepMind), and Copilot (Microsoft Corporation)-using five complex CN cases from our nutrition support service. Each AIA was queried with patient-specific CN questions. Responses were blinded and reviewed by five physician CN experts using an eight-item assessment tool evaluating clarity, relevance, evidence, and clinical utility.
RESULTS: All AIAs produced clinically appropriate responses, with Gemini scoring highest in relevance (4.04) and clarity (4.16). Overall satisfaction scores ranged from 3.08 (Copilot) to 3.84 (Gemini). Citation quality and originality of insights varied and were generally limited, and no consistent differences in performance were observed across the five cases among the four AIAs.
CONCLUSION: LLM-based AIAs can reliably replicate expert reasoning in CN. Although not yet a source of novel clinical insights, the true potential of this approach may lie in its application among physicians without specialized expertise in CN, helping to bridge existing knowledge gaps in nutrition care. Presenting full clinical cases, as shown in this study, could support AIA-enabled e-consultation in the future, thereby addressing gaps in CN education.
METHODS: This retrospective study evaluated four LLM-based AIAs-ChatGPT (OpenAI), OpenEvidence (OpenEvidence Inc), Gemini (Google, Google DeepMind), and Copilot (Microsoft Corporation)-using five complex CN cases from our nutrition support service. Each AIA was queried with patient-specific CN questions. Responses were blinded and reviewed by five physician CN experts using an eight-item assessment tool evaluating clarity, relevance, evidence, and clinical utility.
RESULTS: All AIAs produced clinically appropriate responses, with Gemini scoring highest in relevance (4.04) and clarity (4.16). Overall satisfaction scores ranged from 3.08 (Copilot) to 3.84 (Gemini). Citation quality and originality of insights varied and were generally limited, and no consistent differences in performance were observed across the five cases among the four AIAs.
CONCLUSION: LLM-based AIAs can reliably replicate expert reasoning in CN. Although not yet a source of novel clinical insights, the true potential of this approach may lie in its application among physicians without specialized expertise in CN, helping to bridge existing knowledge gaps in nutrition care. Presenting full clinical cases, as shown in this study, could support AIA-enabled e-consultation in the future, thereby addressing gaps in CN education.