Fundamental Advancements in Structured Reporting in Radiology

The structuring of radiology reports has generated a healthy conversation within the diagnostic medical imaging world. We aim to provide a narrative review of fundamental advancements in structured radiology reporting, along with their benefits and drawbacks, placing special emphasis on the significance of report composition, format, and language, which can assist radiologists and the multidisciplinary medical team to communicate more clearly and efficiently. The narrative’s findings and data were primarily derived from scholarly, peer-reviewed journals such as PubMed, PubMed Central, Google Scholar, and MEDLINE, although we also made use of ephemeral publications and search engines such as Google. Among the search phrases utilized were artificial intelligence, diagnostic medical imaging, fundamental advancements, standardization and structured radiology reports. The worries, questions, and limitations of radiology report structuring on the other hand, include potential user experience problems, limited flexibility of the report template’s contents and reluctance to change, particularly among the older radiologists. We look at the efforts made towards structured radiology reports, which include utilizing standard imaging lexicons, creating reporting templates and incorporating clinical decision support tools. A lot of work is still required from radiologists, healthcare providers, and software developers to make sure that the structured output is eventually accepted, even though it can be laborious and expensive because of challenges like the requirement for testing, authenticating, custom mapping and debugging metrics. The potential for modern technological advancement to enhance the structured reporting process is examined, including artificial intelligent (AI) driven solutions. The review concludes by outlining some of the fundamental advances in structured reporting in radiology, including contextual reporting, natural language processing, common data components, RSNA radiology reporting initiatives, Radlex, BI-RADS, and the most recent, CAD-RADS, as well as AI-reporting solutions, emphasizing their benefits and drawbacks whilst also paying special attention to the significance of report composition, format and language.