The Digital Press

Generative AI can simplify health information efficiently but often omits crucial details, impacting comprehension. This study evaluates methods to detect and reinsert missing information into AI-simplified health texts to improve accuracy and completeness.

Why it matters: Accurate health information is vital for patient understanding and decision-making, so missing details can hinder effective care.

The big picture: Adding all missing entities back into simplified texts significantly improves content fidelity over partial or random additions.

Stunning stat: Reconstructed texts with all missing entities show higher semantic similarity and content overlap than other tested methods.

Quick takeaway: Current AI tools detect missing entities well but struggle to rank their importance for optimal text restoration.