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NLP Powered Orthopaedics Expert System
0
Zitationen
4
Autoren
2026
Jahr
Abstract
In orthopaedic care, long wait times for initial meetings, unclear communication, and imprecise initial evaluations are common issues. This study suggests an Orthopaedics Expert System that uses NLP, integrating speech recognition, natural language processing (spaCy, BioBERT), and machine learning (Random Forest) to help automate diagnoses and treatment suggestions. The system changes patient voice inputs into text, pulls out clinical symptoms using NLP models, and compares them to a detailed orthopaedic knowledge base. A set of consultation records was used to train and check the models. A reasoning layer based on rules makes sure of medical accuracy and clarity. The system, set up using Streamlit, got a 92.4% accuracy rate on test data, making patient-doctor interactions better. This system improves how correct diagnoses are, cuts down on appointment delays, and helps orthopaedic doctors make choices using a clear, databased method.
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