MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning
Medical generative AI often fails to simultaneously achieve personalization and readability, particularly for users with varying health literacy levels. Method: This paper introduces the first readability-controllable medical instruction-tuning framework, integrating human feedback–driven readability-level annotation, multi-task instruction tuning, and semantics-preserving text rewriting to enable controllable generation that balances complexity, clinical accuracy, and reliability. Contribution/Results: The framework demonstrates strong cross-task and cross-domain generalization, outperforming GPT-4 across nine medical benchmarks: readability instruction error rate decreases by 12.6% (1.39 vs. 1.59), clinical relevance (ROUGE-L) improves by 14.7 points, expert preference reaches 71.7%, and comprehension among low-literacy users is significantly enhanced.