Integration der strukturierten Befundung in den radiologischen Routine-Workflow

Kim, Su Hwan; Mir-Bashiri, Sanas; Matthies, Philipp; Sommer, Wieland H.; Nörenberg, Dominik

doi:10.1007/s00117-021-00917-0

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Moreover, successful clinical implementation of AI platform solutions is strongly dependent on seamless workflow integration, which requires few, if any, extra interactions by radiologists. Commercial SR platforms could offer additional advantages here, particularly due to their high interoperability standards, their widespread use and availability of large amounts of customizable templates [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

Jorg,

Halfmann,

Stoehr

et al. 2024

Insights Imaging

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Objectives Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. Methods Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. Results Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). Conclusion The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. Critical relevance statement With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. Key points • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

Jorg,

Halfmann,

Stoehr

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

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“…Furthermore, platforms should be created to allow for healthcare professionals to exchange validated and effective prompts, similar to previous initiatives by radiological societies for sharing structured reporting templates(35)(36)(37).Unlike diagnostic performance, reading times did not improve with either of the LLMassisted workflows. As prior work on AI-based image analysis algorithms and structured reporting has illustrated(38,39), integration of technologies into the local IT infrastructure is critical for user acceptance and can boost efficiency. Vendors of radiology reporting solutions and Picture Archiving and Communication Systems (PACS) should explore ways to seamlessly embed LLM-based features supporting differential diagnosis and other tasks.…”

mentioning

confidence: 99%

Boosting LLM-Assisted Diagnosis: 10-Minute LLM Tutorial Elevates Radiology Residents’ Performance in Brain MRI Interpretation

Kim,

Schramm,

Wihl

et al. 2024

Preprint

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PurposeTo evaluate the impact of a structured tutorial on the use of a large language model (LLM)-based search engine on radiology residents’ performance in LLM-assisted brain MRI differential diagnosis.Materials & MethodsIn this retrospective study, nine radiology residents determined the three most likely differential diagnoses for three sets of ten brain MRI cases with a challenging yet definite diagnosis. Each set of cases was assessed 1) with the support of conventional internet search, 2) using an LLM-based search engine (© Perplexity AI) without prior training, or 3) with LLM assistance after a structured 10-minute tutorial on how to effectively use the tool for differential diagnosis. The tutorial content was based on the results of two studies on LLM-assisted radiological diagnosis and included a prompt template. Reader responses were rated using a binary and numeric scoring system. Reading times were tracked and confidence levels were recorded on a 5-point Likert scale. Binary and numeric scores were analyzed using chi-square tests and pairwise Mann-Whitney U tests each. Search engine logs were examined to quantify user interaction metrics, and to identify hallucinations and misinterpretations in LLM responses.ResultsRadiology residents achieved the highest accuracy when employing the LLM-based search engine following the tutorial, indicating the correct diagnosis among the top three differential diagnoses in 62.5% of cases (55/88). This was followed by the LLM-assisted workflow before the tutorial (44.8%; 39/87) and the conventional internet search workflow (32.2%; 28/87). The LLM tutorial led to significantly higher performance (binary scores: p = 0.042, numeric scores: p = 0.016) and confidence (p = 0.006) but resulted in no relevant differences in reading times. Hallucinations were found in 5.1% of LLM queries.ConclusionA structured 10-minute LLM tutorial increased performance and confidence levels in LLM-assisted brain MRI differential diagnosis among radiology residents.Clinical Relevance StatementOur findings highlight the considerable benefits that even low-cost, low-effort educational interventions on LLMs can provide. Integrating LLM education in radiology training programs could augment practitioners’ capacity to harness AI technologies effectively.

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Integration von KI-Anwendungen der bildgebenden Diagnostik – Ein Praxisbericht

Bröhan,

Liguori

2024

Angewandte Nuklearmedizin

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ZusammenfassungTrotz des Wachstums an verfügbaren KI-Produkten wird eine Diskrepanz zwischen deren Verfügbarkeit und der täglichen Nutzung in Kliniken festgestellt. Der Artikel beleuchtet die Integration von KI-Anwendungen der bildgebenden Diagnostik in den klinischen Regelbetrieb und beschreibt wie klinische Nutzungspotenziale durch gezieltes Management erschlossen werden können. Der Beitrag legt den Fokus dabei auf konkrete Maßnahmen und Entscheidungsprozesse, die notwendig sind, um KI erfolgreich in die klinische Routine zu integrieren und gibt Einblicke in Beispiele aus der Praxis innerhalb eines Gesundheitskonzerns.

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Integration der strukturierten Befundung in den radiologischen Routine-Workflow

Cited by 3 publications

References 30 publications

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

Boosting LLM-Assisted Diagnosis: 10-Minute LLM Tutorial Elevates Radiology Residents’ Performance in Brain MRI Interpretation

Integration von KI-Anwendungen der bildgebenden Diagnostik – Ein Praxisbericht

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