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MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning.

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Generative AI in healthcare needs clear communication. MedReadCtrl, a new framework, adjusts AI output complexity for better understanding without losing meaning, improving patient education and access to care.

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Natural Language Processing

Background:

  • Generative AI shows promise in healthcare applications like clinical decision support and patient engagement.
  • Effective human-AI communication is crucial for deploying AI in healthcare, requiring personalized and understandable content.
  • Current AI models struggle to adjust output complexity while maintaining medical accuracy and intent.

Purpose of the Study:

  • To introduce MedReadCtrl, a novel readability-controlled instruction tuning framework for Large Language Models (LLMs).
  • To enable LLMs to dynamically adjust output complexity for improved patient comprehension without sacrificing medical meaning.
  • To evaluate MedReadCtrl's effectiveness in enhancing human-AI communication for healthcare applications.

Main Methods:

  • Developed MedReadCtrl, a framework for instruction tuning LLMs with readability control.
  • Evaluated MedReadCtrl on nine datasets and three tasks across medical and general domains.
  • Compared MedReadCtrl's performance against GPT-4 in terms of readability instruction-following errors and clinical task performance.

Main Results:

  • MedReadCtrl demonstrated significantly fewer readability instruction-following errors compared to GPT-4 (1.39 vs. 1.59 on ReadMe, p<0.001).
  • The framework achieved substantial performance gains on unseen clinical tasks, including +14.7 ROUGE-L and +6.18 SARI on MTSamples.
  • Medical experts consistently preferred MedReadCtrl outputs (71.7% vs. 23.3%), particularly for low-literacy audiences.

Conclusions:

  • MedReadCtrl effectively restructures clinical content into accessible language, aligning with readability targets while preserving medical intent.
  • The framework offers a scalable solution for enhancing patient education and promoting equitable access to AI-driven healthcare.
  • Improved human-AI communication through readability control is vital for successful AI deployment in clinical settings.