• 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.

If you have any feedback or thoughts about the app, feel free to leave a comment below.