The Quest for Widespread Adoption of Structured Reporting in Radiology

Overview

Despite longstanding recognition of its benefits, structured reporting in radiology has yet to achieve widespread adoption in clinical practice. Recent advances, including large language models, offer promising solutions to overcome workflow disruptions and facilitate integration, but challenges remain in harmonization, incentives, and equitable access.

Background

Structured reporting in radiology has been compared to a fusion reactor—promising transformative benefits but facing persistent practical challenges. Over the past decade, efforts have focused on developing templates and demonstrating advantages, yet adoption remains limited globally. The European Society of Radiology highlights the need for harmonization of templates and incentives to encourage uptake. Emerging AI technologies, particularly large language models, may help bridge the gap by enabling seamless conversion of free-text reports into structured formats without disrupting radiologists' workflows.

Data Highlights

An informal survey by the European Society of Radiology revealed that many national societies are independently collecting structured reporting templates, but cross-institutional applications and incentives for usage are largely absent outside the USA. The adoption of AI-powered solutions is anticipated soon, though economic and ethical challenges persist.

Key Findings

• Structured reporting remains underutilized despite recognized benefits and a decade of development efforts.
• National radiological societies often work independently on template creation, lacking harmonization and cross-institutional use.
• Workflow disruption and increased reporting time have been major barriers to adoption among radiologists.
• Large language models show promise in converting free-text reports into structured formats, potentially easing transition challenges.
• Reliance on commercial AI solutions may introduce vendor lock-in, economic burdens, and exacerbate global disparities.
• Strong incentives or disincentives will be necessary to motivate radiologists and healthcare systems to adopt structured reporting widely.

Clinical Implications

Clinicians and healthcare administrators should recognize that structured reporting can enhance data utility and patient care but requires coordinated efforts for template standardization and resource allocation. Adoption strategies should consider integrating AI tools to minimize workflow disruption while addressing potential economic and ethical concerns. Policymakers must be engaged to provide incentives that support radiologists during the transition phase.

Conclusion

Structured reporting in radiology holds transformative potential but remains an ongoing challenge requiring harmonization, incentives, and technological innovation. Advances in AI may accelerate adoption, yet sustained commitment from all stakeholders is essential to realize its full benefits.

References

1. Bosmans et al 2014 -- Structured reporting in radiology: the fusion reactor analogy
2. Fusion Reactor Status 2023 -- Sustained net power production challenges
3. European Society of Radiology 2018 -- State of structured reporting in Europe
4. European Society of Radiology Update 2023 -- Structured reporting progress and challenges
5. Automated scheduling of incidental nodule follow-up -- Data-driven healthcare applications
6. Workflow disruption concerns in structured reporting adoption
7. Reluctance to alter speech-based workflows in radiology
8. Large language models in radiology reporting -- Emerging solutions
9. LLMs structuring unstructured reports -- Technical feasibility
10. AI and fusion power analogy -- Engineering challenges solved by AI
11. Potential biases and disparities from AI-based reporting solutions