Clinical Obesity Through the Lens of Context-Aware Large Language Models

To explore whether large language models can assist in refining complex medical definitions, this proof-of-concept study evaluated the capacity of three advanced systems, ChatGPT-4, Gemini 1.5 Pro, and Grok 3, to interpret, critique, and build upon existing definitions of clinical obesity, with particular reference to the Lancet Commission on the Definition and Diagnosis of Clinical Obesity.

Five structured questions were used in a three-stage qualitative design: (1) baseline responses without external references, (2) context-informed responses incorporating the Lancet Commission report, and (3) generative redefinition of clinical obesity. Model outputs were reviewed by a medical expert for accuracy, originality, and concordance with established guidelines.

Without contextual guidance, large language models produced generalized definitions largely reflecting public-domain sources. Supplying the Lancet Commission report yielded more precise, conceptually consistent, and clinically relevant outputs. In the generative phase, all models proposed refined definitions that addressed recognized gaps in existing frameworks, emphasizing functional impairment, psychosocial dimensions, and the limitations of body mass index as a diagnostic criterion.

When anchored to authoritative, domain-specific evidence, large language models can serve as adjunctive tools for knowledge synthesis, clarifying and strengthening complex medical constructs. Integrating human expertise with artificial intelligence under transparent and ethically governed conditions may enable the development of more inclusive, adaptive, and evidence-based clinical definitions.