The radiology report is a vital tool for guiding patient care, yet radiologists often lack formal training in reporting. While adhering to key principles can promote clear communication of imaging findings, impressions, and recommendations, crafting a report that meets the needs of various audiences can be challenging and errors can negatively affect patient care.

As a result, radiologists spend up to 30% of their time proofreading and correcting reports for potential mistakes. That is partly because existing solutions do not serve the needs of clinicians for automated correction and formatting of their radiology reports, leading to high reporting time. And crucially, their features are not tailored to the highly individualized SOPs of every institution, their data, subdisciplines, and to the German language setup.

Team RadiologyFlow has developed an AI-powered software that automatically detects and corrects errors in radiology reports. Leveraging large language models trained on vast radiological texts, RadiologyFlow identifies spelling, grammar, terminology, and formatting issues, offering adaptable automated corrections tailored to customers’ data and SOPs. With its easy integration into PACS or RIS systems, the one-click corrections significantly reduce proofreading time everywhere radiology reports are issued.

RadiologyFlow goes further, ensuring corrections align with the radiologist’s intended meaning, reporting style, and the local SOPs. This personalized approach seamlessly integrates with existing speech patterns and workflows, facilitating faster, more accurate report creation.

Learning from interactions, RadiologyFlow evolves into bespoke software personalized for each user. This means radiologists can focus their expertise on high-quality interpretation, where it matters most. Referring physicians receive clear, consistent, error-free reports to guide optimal patient care; radiologists can enjoy greater efficiency and job satisfaction; and patients benefit from quicker answers and better outcomes.

RadiologyFlow’s team combines deep expertise in radiology workflows, AI/NLP, and software engineering derived from Charité’s radiology and neuroradiology departments and is supported by senior radiologists at the German Heart Center in Munich and TUM.
RadiologyFlow’s mission is to rid the world of radiology reporting errors—one AI-assisted report at a time.