A Comparative Analysis of Ai-Based Solutions for Clinical Documentation

Healthcare systems handle thousands of documents daily across various departments, requiring some effort during the digitalization processes. One strategy employed by medical staff is recording appointments for later transcription. However, this process is time-consuming and not practical for all scenarios. In this paper, we present a comprehensive methodology for converting medical audio recordings into structured documentation through multiple AI-based solutions. We propose and evaluate three distinct methods: a baseline two-stage pipeline using Mixtral 7B and Llama 70B models, a cyclic LLM approach leveraging self-improvement loops, and an embedding-based retrieval system utilizing BGE M3. Our experimental results show that the RBF kernel consistently outperformed linear kernels and logistic regression approaches across all metrics, maintaining high precision (0.87-0.94) and perfect recall.