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Synthesizing evidence regarding artificial intelligence generated radiological reports based on medical images: a scoping review
ABSTRACT
Background:
Radiological reports are essential for clinical diagnosis. However, their preparation is laborious and time-consuming for radiologists. Artificial intelligence (AI) has the potential to help radiologists reduce this workload.
Objective:
This scoping review aims to map current research on AI-based radiological report generation, highlighting its applications, limitations, and future directions.
Methods:
We systematically retrieved 321 records from three major scientific databases (PubMed, Embase, Web of Science) and included 58 studies after screening. Benchmark datasets, radiological subspecialties, AI models, output formats, and evaluation methods were extracted and analyzed.
Results:
Nine public benchmark datasets and six radiological subspecialties were identified. Transformer-based architectures have become the dominant approach, with outputs primarily generated in natural language. Dataset diversity is limited, with most studies relying on chest X-ray images from two public datasets. Structured outputs for machine readability remain underexplored, and expert human evaluation is infrequently used as an assessment method.
Conclusions:
Current AI research in radiological report generation is constrained by limited dataset diversity and evaluation practices. Future studies should expand imaging modalities and subspecialties, adopt unified vision-language Transformer models, generate more structured outputs for programmatic use, and incorporate expert human evaluation for quality assessment.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.