Utilizing large language models for gastroenterology research: a conceptual framework.

Area of Science:

• Artificial Intelligence in Medicine
• Gastroenterology Innovations
• Clinical Decision Support Systems

Background:

• Large language models (LLMs) are transforming healthcare, offering assistance in clinical decision-making, research, and patient management.
• In gastroenterology, LLMs show promise for clinical decision support, data extraction, and patient education, but face challenges like bias and regulatory hurdles.

Purpose of the Study:

• To present a structured framework for integrating LLMs into gastroenterology practice.
• To demonstrate a real-world application of this framework using Hepatitis C treatment.
• To ensure accuracy, safety, and clinical relevance while mitigating AI risks.

Main Methods:

• Framework development encompassing goal definition, team assembly, data handling, model selection, fine-tuning, calibration, and hallucination mitigation.
• Evaluation of retrieval-augmented generation and fine-tuning for model adaptability.
• Incorporation of bias detection, reinforcement learning from human feedback, and structured prompt engineering for reliability.
• Addressing ethical and regulatory considerations (HIPAA, GDPR, DECIDE-AI, SPIRIT-AI, CONSORT-AI).

Main Results:

• The framework provides a comprehensive approach to LLM integration in gastroenterology.
• Specific methods are detailed for enhancing LLM accuracy, safety, and reliability in clinical settings.
• Ethical and regulatory compliance strategies are integrated throughout the framework.

Conclusions:

• LLMs hold substantial potential to enhance decision-making, research efficiency, and patient care in gastroenterology.
• Responsible deployment necessitates rigorous bias mitigation, transparency, and continuous validation.
• Future research should prioritize multi-institutional validation and AI-assisted clinical trials for establishing LLMs as trusted tools.