# PRECISE Framework: GPT-based Text For Improved Readability, Reliability, and Understandability of Radiology Reports For Patient-Centered Care

arXiv: https://arxiv.org/abs/2403.00788 · HTML: https://mlsanity.com/paper/2403.00788/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Gpt-4: A Review on Advancements and Opportunities in Natural Language Processing](https://mlsanity.com/paper/2305.03195/) (2023)

## Abstract (arXiv)

This study introduces and evaluates the PRECISE framework, utilizing OpenAI's GPT-4 to enhance patient engagement by providing clearer and more accessible chest X-ray reports at a sixth-grade reading level. The framework was tested on 500 reports, demonstrating significant improvements in readability, reliability, and understandability. Statistical analyses confirmed the effectiveness of the PRECISE approach, highlighting its potential to foster patient-centric care delivery in healthcare decision-making.