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The "AI-Powered Radiology Report Structuring Tool" application is designed to enhance the utility of radiological documentation by transforming unstructured, narrative-style radiology reports into a structured, standardized format. By leveraging the OpenAI GPT-4 API, the application interprets free-text reports—submitted as .pdf, .docx, .txt files or directly pasted text—and systematically organizes the information into distinct fields. This structured output facilitates easier interpretation, comparison, and integration into health records systems. Disclaimer: Please don't enter/upload any personal or sensitive information in the app.

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Use Cases Limitations Evidence Owner's Insight
  • Clinical Decision Support: Clinicians can quickly understand key findings and make informed decisions without navigating through dense paragraphs.
  • Research: Researchers can efficiently extract data for analysis and machine learning models, potentially contributing to medical advancements.
  • Health Records Management: Health information managers can streamline the incorporation of radiology reports into electronic health records (EHRs), ensuring consistency and accessibility.
  • Quality Assurance: Radiology departments can utilize structured reports for quality control, ensuring all necessary information is consistently documented.
  • Data Security/Privacy: OpenAI doesn’t use the data sent via the API to train/improve its models and the data is only retained for 30 days to monitor for abuse before being deleted, However, don't enter sensitive/identifiable information into the app or any third-person API. For more information on how OpenAI handles information sent via its API, see the “API Platform FAQ” section of https://openai.com/enterprise-privacy.
  • Variability in Language: The app may struggle with ambiguous language or idiomatic expressions common in free-text reports.
  • Complex Cases: Highly complex cases with nuanced details may not be fully captured in a standardized template.
  • Template Rigidity: The reliance on templates may omit novel findings or rare conditions that do not fit predefined fields.

The core functionality/prompts used are based on the paper Leveraging GPT-4 for Post Hoc Transformation of Free-text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study, published in April 2023 and cited by 51+ different papers so far. The corresponding Github repository was created by Keno Bressem, a board-certified radiologist; for more information about him, please see https://aim.hms.harvard.edu/team/keno-bressem.

Keno Bressem, a board-certified radiologist, created the app’s core functionality, which has been peer-reviewed. See https://aim.hms.harvard.edu/team/keno-bressem for more information about him.

Venkata Chengalvala created this app’s Steramlit interface. As an AI consultant for Health Universe, he ports high-quality peer-reviewed AI models helpful to clinicians, patients, and/or researchers to Health Universe's platform. He has a Bachelor of Science in Molecular, Cellular, and Developmental Biology (MCDB) and Computer Science from the University of Michigan-Ann Arbor. During his undergraduate education, he engaged in medical research, co-authoring a literature review on tumor-derived exosomes that’s cited by 30+ people so far: https://www.sciencedirect.com/science/article/pii/S2211383521001398.

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Peer reviewed

Warning: This application or model has been peer reviewed, but still may occasionally produce unsafe outputs.


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  • Diagnostics & Imaging

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Venkata Chengalvala

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