Systems review demonstrates that human-in-the-loop AI dictation improves reporting efficiency in radiology workflows, suggesting integrated systems overcome documentation bottlenecks.
Radiology reporting remains a major workflow bottleneck, with inefficiencies in dictation, editing, and report finalization leading to delays and an increased documentation burden. AI-enabled dictation systems combine automatic speech recognition (ASR), natural language processing (NLP), structured reporting, and real-time quality assurance to streamline workflows while preserving radiologist oversight. Current evidence suggests that human-in-the-loop systems improve reporting efficiency, consistency, and documentation quality more effectively than standalone speech recognition or fully automated reporting approaches. In India, implementation presents unique challenges related to multilingual clinical environments, infrastructure variability, and evolving digital health frameworks such as the Ayushman Bharat Digital Mission. Successful adoption, therefore, depends not only on model performance but also on workflow integration, interoperability, governance, and local validation. AI-enabled dictation represents a shift from simple speech-to-text transcription to structured, interoperable, and clinically integrated reporting systems that have the potential to improve workflow efficiency, report quality, and clinical communication.
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Tripathi et al. (2026) studied this question.
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