Large language models and the future of gastroenterology: dissecting the biopolitics of data in a global health ecosystem

Large language model (LLM) chatbots such as ChatGPT have moved from being a representation of technological novelty to working in clinical conversation in scarcely two years, fuelling hopes that conversational artificial intelligence will democratize expertise across gastroenterology and digestive endoscopy. A recent systematic review cataloged emerging LLM applications ranging from automated endoscopy report generation and guideline-concordant triage advice to interactive educational support for endoscopy teams. Emerging early evidence suggests that multimodal LLM frameworks now achieve near-expert accuracy in endoscopic lesion recognition while also producing plain-language explanations for patients, indicating that perhaps a single conversational platform could eventually unite diagnosis, structured documentation, and patient education. Yet the diffusion of any medical technology is shaped by social context, environmental externalities, and the epistemic boundaries of the data that train it. Taking stock of those wider forces is therefore essential if LLMs are to advance digestive health rather than reproduce existing inequities. By biopolitics we refer to the ensemble of policies, infrastructures, and market logics through which institutions manage bodies and populations, thus determining whose data are captured, which risks are rendered visible, and how care, surveillance, and environmental costs are distributed. Read through this lens, LLMs are not neutral engineering artifacts but instruments that channel attention and resources via training-data curation, platform governance, and procurement choices, with downstream consequences for equity in health outcomes.