Improved healthcare diagnosis accuracy through the application of deep learning techniques in medical transcription for disease identification

Medical transcription plays a pivotal role in healthcare by meticulously documenting patient interactions, diagnoses, treatments, and vital medical details. These records are indispensable for healthcare practitioners, facilitating high-quality patient care, optimizing clinical decision-making, and ensuring adherence to regulatory standards. While traditional manual transcription methods have been valuable, they come with inherent drawbacks, including the potential for errors, time-intensive processes, and the necessity for skilled transcriptionists. This paper proposes a cutting-edge solution that integrates deep learning techniques, specifically Automatic Speech Recognition (ASR) and a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) architecture, into medical transcription and disease verification processes. These advanced neural network structures can comprehend and extract relevant medical information from text, including diagnoses, treatment courses, and patient histories. The automated, data-driven framework is developing due to the increasing pressure for better and faster diagnostic procedures in the medical field. In the framework of the study, tagged voice recordings related to different disorders are used: it proves that the integration of ASR with CNN-LSTM increases the accuracy and productivity of medical transcriptions, reaching 99 percent accuracy. This approach represents a paradigm shift in the field and successfully resolves the constraints that were noteworthy in prior methods.

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