Medical Specialty Classification: An Interactive Application with Iterative Improvement for Patient Triage

Abstract

The challenge of accurately identifying the appropriate medical specialty based on patient symptoms leads to delays in diagnosis and treatment. This paper presents an AI model developed to classify medical specialties from symptom descriptions. The model, implemented with BERT, hosted via a Python-based Flask API v3, and integrated with an interactive frontend application, allows users to input symptoms textually or interactively select affected body parts and answer multiple choice questions. Following deployment, feedback data from doctors and residents was collected and utilized to enhance the model performance, supplemented by additional data from online medical forums. This study demonstrates significant improvements in finding the correct medical specialty, contributing to more efficient patient triage, reducing the time to diagnose and treat patients, and eliminating the presence of doctors in the initial process as they are often busy in emergency departments. The use of generative AI and large language models, notably BERT, is highlighted as a key factor in the model’s success.