Structured Reporting in Radiology

Structured reporting in radiology refers to the use of predefined templates and standardised language to generate imaging reports that are complete, consistent and machine-readable. By organising findings into uniform sections and employing controlled terminology, structured reports reduce ambiguity, ensure that all pertinent observations are addressed and facilitate communication between radiologists, clinicians and other stakeholders. The approach supports automated functions such as staging algorithms and radiomics feature extraction, enables integration with electronic patient records and laboratory data, and simplifies data sharing for research, quality assurance and clinical registries. International and inter-societal efforts have produced modular templates adaptable to local workflows, and software tools now allow seamless incorporation of structured reporting into picture-archiving and communication systems. Despite clear benefits in report completeness, clarity and data interoperability, challenges remain in balancing template rigidity with reporting flexibility, achieving widespread adoption among practitioners and ensuring compatibility across diverse information-technology environments. Ongoing work focuses on refining template design, enhancing user interfaces and embedding decision support to optimise the value of structured reporting for patient care and scientific discovery.

Research from Nature Portfolio

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Research from all publishers

Recent systematic analyses have evaluated the evidence base for structured reporting, distinguishing between level 1 implementations that use templates and checklists and level 2 approaches that employ interactive drop-down menus or decision trees. Findings indicate that, although adoption is growing—especially in abdominal and neuro­radiology—high-level evidence for clinical impact remains limited and should be interpreted with caution. In modality-specific studies, the use of structured report data as training labels for deep learning models has been demonstrated in ankle radiographs, achieving robust fracture-detection performance and illustrating how structured outputs can accelerate algorithm development. Comparative assessments of head and neck ultrasound reports have shown that structured formats deliver significantly higher completeness and readability scores than free-text reports, albeit with a modest increase in reporting time, and yield high inter-rater reliability. These diverse studies underscore the potential of structured reporting to enhance data quality, support artificial intelligence applications and improve interdisciplinary communication, while highlighting the need for further validation of clinical outcomes and user-centred design optimisation.

Structured Reporting in Radiology publication trend

The graph below shows the total number of articles in structured reporting in radiology across all publications each year (not limited to Nature Index journals).

Technical terms

Structured reporting: The process of generating radiology reports using predefined templates and standardised terminology to ensure completeness and consistency.

Structured layout: A level 1 approach in which a fixed template or checklist guides the organisation of report sections without interactive content controls.

Structured content: A level 2 approach that integrates interactive elements such as drop-down menus, point-and-click options or decision trees to populate report fields.

Radiomics: The extraction and analysis of quantitative features from medical images to support diagnostic, prognostic and predictive modelling.

Deep learning: A subset of machine learning employing neural network architectures to learn hierarchical representations from large volumes of labelled imaging data.

References

1. ESR paper on structured reporting in radiology. Insights into Imaging (2018).
2. Structured reporting: if, why, when, how—and at what expense? Results of a focus group meeting of radiology professionals from eight countries. Insights into Imaging (2012).
3. Big data, artificial intelligence, and structured reporting. European Radiology Experimental (2018).
4. Redefining the structure of structured reporting in radiology. Insights into Imaging (2020).
5. Structured reporting in radiology: a systematic review to explore its potential. European Radiology (2021).
6. Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs. Insights into Imaging (2019).
7. Structured reporting of head and neck ultrasound examinations. BMC Medical Imaging (2019).