Utilizing large language models for gastroenterology research: a conceptual framework

Abstract

Large language models (LLMs) transform healthcare by assisting clinicians with decision-making, research, and patient management. In gastroenterology, LLMs have shown potential in clinical decision support, data extraction, and patient education. However, challenges such as bias, hallucinations, integration with clinical workflows, and regulatory compliance must be addressed for safe and effective implementation. This manuscript presents a structured framework for integrating LLMs into gastroenterology, using Hepatitis C treatment as a real-world application. The framework outlines key steps to ensure accuracy, safety, and clinical relevance while mitigating risks associated with artificial intelligence (AI)-driven healthcare tools. The framework includes defining clinical goals, assembling a multidisciplinary team, data collection and preparation, model selection, fine-tuning, calibration, hallucination mitigation, user interface development, integration with electronic health records, real-world validation, and continuous improvement. Retrieval-augmented generation and fine-tuning approaches are evaluated for optimizing model adaptability. Bias detection, reinforcement learning from human feedback, and structured prompt engineering are incorporated to enhance reliability. Ethical and regulatory considerations, including the Health Insurance Portability and Accountability Act, General Data Protection Regulation, and AI-specific guidelines (DECIDE-AI, SPIRIT-AI, CONSORT-AI), are addressed to ensure responsible AI deployment. LLMs have the potential to enhance decision-making, research efficiency, and patient care in gastroenterology, but responsible deployment requires bias mitigation, transparency, and ongoing validation. Future research should focus on multi-institutional validation and AI-assisted clinical trials to establish LLMs as reliable tools in gastroenterology.

Keywords: artificial intelligence; framework; generative artificial intelligence; healthcare.

Figure 1.

This figure illustrates a step-by-step framework for developing an LLM-powered clinical decision support system for Hepatitis C treatment. It outlines key stages, including ethical approvals, data collection, model selection, calibration, user interface development, integration with EHRs, and continuous improvement. EHR, electronic health records; LLM, large language models.

Figure 2.

This figure presents a wireframe of the LLM-driven clinical decision support interface for Hepatitis C management. It demonstrates structured data entry fields for patient ID, genotype, fibrosis score, co-morbidities, and HCV RNA levels. The interface pre-populates critical fields, integrates biopsy/imaging uploads, and generates personalized treatment recommendations based on clinical guidelines. A feedback loop allows clinicians to refine model outputs, enhancing reliability and usability. LLM, large language models.