Abstract Medical transcription is an intrinsic part of clinical documentation, formerly reliant on human transcriptionists or simple speech recognition technology. However, these methods inevitably fall short of alleviating the mounting documentation burden, largely responsible for clinician burnout and reduced health care efficiency. The advent of artificial intelligence (AI), specifically natural language processing and automatic speech recognition, has revolutionized the practice of medical transcription by enabling real-time, context-sensitive, and structured clinical documentation. This article reviews the integration of AI in medical transcription across different clinical environments, including outpatient encounters, radiology, and emergency care. By leveraging large language models and deep learning, AI applications enhance documentation quality, reduce cognitive load, and enrich clinical workflows with features such as the International Classification of Diseases coding, letter generation, and after-visit summaries. Expanding into research, applications involve large-scale data extraction, manuscript preparation, and cross-disciplinary collaboration. Radiology, with its high-volume and precision-rich reporting requirements, will benefit significantly from the application of AI-based tools such as Rad AI, VoxRad, and multilingual tools such as Whisper. Challenges remain in spite of the above advancements. Accuracy, hallucination, data confidentiality, regulation, and physician confidence problems remain hindrances to widespread adoption. In addition, the consolidation of neural interface technologies such as brain–computer interfaces presents both promising possibilities and ethical subtleties for further development. This article critically examines the existing applications, advantages, and limitations of AI-based transcription systems, as well as their potential to revolutionize the future of clinical documentation and health care provision.
Mehta et al. (2026) studied this question.