Enhancing Orthopedic Knowledge Assessments:The Performance of Specialized Generative Language Model Optimization

Objective This study aimed to evaluate and compare the effectiveness of knowledge base-optimized and unoptimized large language models(LLMs)in the field of orthopedics to explore optimization strategies for the application of LLMs in specific fields.Methods This research constructed a specialized knowledge base using clinical guidelines from the American Academy of Orthopaedic Surgeons(AAOS)and authoritative orthopedic publications.A total of 30 orthopedic-related questions covering aspects such as anatomical knowledge,disease diagnosis,fracture classification,treatment options,and surgical techniques were input into both the knowledge base-optimized and unoptimized versions of the GPT-4,ChatGLM,and Spark LLM,with their generated responses recorded.The overall quality,accuracy,and comprehensiveness of these responses were evaluated by 3 experienced orthopedic surgeons.Results Compared with their unoptimized LLMs,the optimized version of GPT-4 showed improvements of 15.3%in overall quality,12.5%in accuracy,and 12.8%in comprehensiveness;ChatGLM showed improvements of 24.8%,16.1%,and 19.6%,respectively;and Spark LLM showed improvements of 6.5%,14.5%,and 24.7%,respectively.Conclusion The optimization of knowledge bases significantly enhances the quality,accuracy,and comprehensiveness of the responses provided by the 3 models in the orthopedic field.Therefore,knowledge base optimization is an effective method for improving the performance of LLMs in specific fields.

Application of Artificial Intelligence in Medicine: An Overview

Artificial intelligence(AI)is a new technical discipline that uses computer technology to research and develop the theory,method,technique,and application system for the simulation,extension,and expansion of human intelligence.With the assistance of new AI technology,the traditional medical environment has changed a lot.For example,a patient’s diagnosis based on radiological,pathological,endoscopic,ultrasonographic,and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload.The medical treatments during the perioperative period,including the preoperative preparation,surgical period,and postoperative recovery period,have been significantly enhanced with better surgical effects.In addition,AI technology has also played a crucial role in medical drug production,medical management,and medical education,taking them into a new direction.The purpose of this review is to introduce the application of AI in medicine and to provide an outiook of future trends.

Application and Development of Intelligent Medicine in Traditional Chinese Medicine

As modern science and technology constantly progresses,the fields of artificial intelligence,mixed reality technology,remote technology,etc.have rapidly developed.Meanwhile,these technologies have been gradually applied to the medical field,leading to the development of intelligent medicine.What’s more,intelligent medicine has greatly promoted the development of traditional Chinese medicine(TCM),causing huge changes in the diagnosis of TCM ailments,remote treatment,teaching,etc.Therefore,there are both opportunities and challenges for inheriting and developing TCM.Herein,the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed,with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians

Objective:To explore a new artificial intelligence(AI)-aided method to assist the clinical diagnosis of tibial plateau fractures(TPFs)and further measure its validity and feasibility.Methods:A total of 542 X-rays of TPFs were collected as a reference database.An AI algorithm(RetinaNet)was trained to analyze and detect TPF on the X-rays.The ability of the AI algorithm was determined by indexes such as detection accuracy and time taken for analysis.The algorithm performance was also compared with orthopedic physicians.Results:The AI algorithm showed a detection accuracy of 0.91 for the identification of TPF,which was similar to the performance of orthopedic physicians(0.92±0.03).The average time spent for analysis of the AI was 0.56 s,which was 16 times faster than human performance(8.44±3.26 s).Conclusion:The AI algorithm is a valid and efficient method for the clinical diagnosis of TPF.It can be a useful assistant for orthopedic physicians,which largely promotes clinical workflow and further guarantees the health and security of patients.