Streamlining Radiology Reporting: A hands-free approach with voice-to-text and generative AI

Abstract

ObjectiveIn radiology, report generation is critical for patient management, though can be time-consuming and labor-intensive. This study aims to investigate the feasibility of a hands-free radiology reporting system, leveraging voice-to-text technology and generative AI to improve time efficiency, report standardization, and reduce radiologist workload. Methods100 chest and musculoskeletal radiographs reports were generated using both traditional methods and an automated process using generative AI. To use generative AI, a radiologist dictated their findings into a voice-to-text software (Dragon), creating a raw text transcript. GPT-4 was then used to develop a report based on our institution’s template. Completion time for each traditional report and voice-to-text transcript was recorded. All reports were then evaluated for comprehensiveness, clarity, factual accuracy, and conciseness on a 5-point Likert scale. ResultsThe AI-driven system demonstrated considerable proficiency in transcribing and generating radiology reports, albeit with occasional inaccuracies. While it achieved a high level of precision in standard scenarios, its performance varied in more complex cases. Notably, the system significantly reduced the time taken to generate reports by 38% (p < 0.0001), signifying a potential increase in overall radiological workflow efficiency. ConclusionThis study highlights the potential of voice-to-text and generative AI integration in radiology reporting. Increased time efficiency, even in standard scenarios, can allow radiologists to focus more on complex cases and patient care. However, further development and refinement are required to address comprehensiveness and occasional inaccuracies. Ongoing advancements in AI and speech recognition technologies suggest a positive outlook for future implementations in medical reporting.