Journal of evaluation in clinical practice · 2026
LLM Chatbot Integration With Inventory Systems for Gastroenterology and Endoscopy Services: A Systematic Review.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Khatri Vinita, Trivedi Niketa
Аннотация
BACKGROUND: Generally, gastroenterology and digestive endoscopy units commonly face constraints. It may be due to a lack of equipment, poor scheduling and inadequate inventory management. These issues not only affect patient throughput but also increase the workload for medical staff. Thus considering the current advancements in artificial intelligence we can work towards new opportunities to enhance clinical pathway and resource optimization to overcome the problems.
OBJECTIVE: The purpose of this systematic review is (i) to study the work done by the researchers in the field of gastroenterology and endoscopy services and LLM-powered chatbots and (ii) to check the possibilities of combining LLM-powered chatbots with traditional and dynamic healthcare inventory models.
METHODS: Common platforms like Google Scholar, PubMed, Scopus, CrossRef and Web of Science were explored for relevant recent literature. The search was conducted using keywords "endoscopy," "chatbots," "large language models," "inventory models," and "gastroenterology." 222 records were collected in total, among which 91 met the required criteria and were subjected to a transparent review process following PRISMA guidelines.
RESULTS: Technical, clinical and organizational issues of LLM implementation in healthcare were addressed in the eligible studies. Slightly less than half of the received papers described predictive scheduling and workflow optimization systems, others addressed research on data management, documentation or interaction with patients. Few of these discussed about moral or legal considerations. The analysis focuses of ability of LLM chatbots to support product forecasting, equipment utilisation, waste reduction and real-time decision support in endoscopy units when integrated with predictive inventory models.
CONCLUSION: The amalgamation of LLM chatbots with inventory management is an effective way to achieve intelligent and sustainable activities in gastroenterology. Even though some problems will arise with this amalgamation, especially when it comes to data interoperability, ethics and validation. These two fields can be combined to work towards more effective, flexible and patient-centric environment.
OBJECTIVE: The purpose of this systematic review is (i) to study the work done by the researchers in the field of gastroenterology and endoscopy services and LLM-powered chatbots and (ii) to check the possibilities of combining LLM-powered chatbots with traditional and dynamic healthcare inventory models.
METHODS: Common platforms like Google Scholar, PubMed, Scopus, CrossRef and Web of Science were explored for relevant recent literature. The search was conducted using keywords "endoscopy," "chatbots," "large language models," "inventory models," and "gastroenterology." 222 records were collected in total, among which 91 met the required criteria and were subjected to a transparent review process following PRISMA guidelines.
RESULTS: Technical, clinical and organizational issues of LLM implementation in healthcare were addressed in the eligible studies. Slightly less than half of the received papers described predictive scheduling and workflow optimization systems, others addressed research on data management, documentation or interaction with patients. Few of these discussed about moral or legal considerations. The analysis focuses of ability of LLM chatbots to support product forecasting, equipment utilisation, waste reduction and real-time decision support in endoscopy units when integrated with predictive inventory models.
CONCLUSION: The amalgamation of LLM chatbots with inventory management is an effective way to achieve intelligent and sustainable activities in gastroenterology. Even though some problems will arise with this amalgamation, especially when it comes to data interoperability, ethics and validation. These two fields can be combined to work towards more effective, flexible and patient-centric environment.