Summary of Precise Framework: Gpt-based Text For Improved Readability, Reliability, and Understandability Of Radiology Reports For Patient-centered Care, by Satvik Tripathi et al.
PRECISE Framework: GPT-based Text For Improved Readability, Reliability, and Understandability of Radiology Reports For Patient-Centered Care
by Satvik Tripathi, Liam Mutter, Meghana Muppuri, Suhani Dheer, Emiliano Garza-Frias, Komal Awan, Aakash Jha, Michael Dezube, Azadeh Tabari, Christopher P. Bridge, Dania Daye
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed PRECISE framework leverages OpenAI’s GPT-4 to improve patient engagement by generating clearer chest X-ray reports at a sixth-grade reading level. The framework is evaluated on 500 reports, showing significant enhancements in readability, reliability, and understandability through statistical analyses. This study highlights the potential of the PRECISE approach to facilitate patient-centric care delivery in healthcare decision-making. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The PRECISE framework helps patients better understand their chest X-ray reports by making them easier to read. Scientists tested this idea on 500 reports and found it worked really well. The reports became more readable, reliable, and easy to understand. This could be an important step forward in helping doctors and patients work together. |
Keywords
* Artificial intelligence * Gpt