The Role of Large Language Models (LLMs) in Hepato-Pancreato-Biliary Surgery: Opportunities and Challenges

Abstract

The integration of large language models (LLMs) into healthcare represents a transformative development in modern medicine, with hepato-pancreato-biliary (HPB) surgery positioned at the forefront of this technological revolution. This editorial examines the multifaceted role of LLMs, including ChatGPT, Claude, and Gemini, in HPB surgical practice, analyzing both their unprecedented opportunities and significant challenges. LLMs demonstrate remarkable potential across multiple domains of HPB surgery, including clinical documentation automation, enhanced surgical decision-making through complex patient data analysis, improved preoperative planning with radiological interpretation, real-time intraoperative decision support, and optimized postoperative patient monitoring with personalized follow-up recommendations. However, significant challenges accompany these opportunities, including data privacy and security concerns, accuracy and reliability issues, particularly regarding artificial intelligence (AI) "hallucinations," ethical considerations involving accountability and algorithmic bias, potential overreliance in surgical education that may compromise critical thinking development, and the risk of exacerbating healthcare disparities in resource-limited settings. Future directions include advancing multimodal learning capabilities, developing specialized HPB-focused LLMs, fostering collaborative development between surgeons and AI researchers, and establishing robust ethical frameworks with regulatory guidelines for safe implementation. The integration of LLMs in HPB surgery represents a paradigm shift requiring thoughtful navigation of opportunities and challenges, with the potential to transform surgical practice while maintaining the highest standards of patient safety and care quality through collaborative efforts among surgeons, AI researchers, ethicists, and policymakers.

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