Deep Learning in Medical Speech to Text: Methods and Challenges

Automated clinical documentation based on clinician-patient conversations is an emerging application of deep learning, driven by advances in medical speech recognition and natural language processing. Despite technological progress, real-world adoption remains limited. This review analyzes deep learning–based medical speech-to-text systems, focusing on methodologies, evaluation strategies, and barriers to clinical implementation. A systematic review of 31 studies was conducted, covering automatic speech recognition, clinical dialogue processing, and large language model-based documentation pipelines. Speech recognition accuracy varies considerably in noisy, multi-speaker, and spontaneous clinical environments. Downstream tasks such as entity extraction and summarization are highly sensitive to transcription errors and constrained by limited real-world datasets. Most systems lack external clinical validation and are tested in controlled settings. Key challenges include speaker diarization, domain adaptation, privacy protection, and the need for standardized evaluation frameworks. Although LLMs demonstrate strong potential, concerns remain regarding hallucinations and factual reliability, necessitating improved robustness and clinician oversight.