In a transformative leap, large language models (LLMs) are reshaping obesity management within healthcare. A systematic review in the International Journal of Obesity analyzes LLM applications in combating the obesity epidemic, a complex condition influenced by genetics, environment, and behavior. Traditional interventions often lack personalization, which LLMs address by synthesizing vast medical datasets to offer tailored dietary plans, behavioral prompts, and predictive modeling. However, challenges exist concerning output quality and inherent biases, necessitating hybrid models that blend LLM insights with clinical expertise. The review emphasizes ethical considerations around data privacy and algorithm transparency, advocating for interdisciplinary collaboration. Additionally, advanced LLM architectures enhance healthcare-specific outputs, integrating telemedicine and continuous patient support. Despite their promise, current LLM applications remain experimental; therefore, rigorous clinical trials are essential. Ultimately, LLMs herald a paradigm shift in personalized care, but a balanced approach must ensure safety, effectiveness, and equitable access in obesity management.
Keywords: AI in obesity, LLM applications, personalized interventions, healthcare technology, predictive modeling, systematic review, ethical AI, behavior modification.
