Development, optimization, and preliminary evaluation of a novel artificial intelligence tool to promote patient health literacy in radiology reports: The Rads-Lit tool - Rushabh H. Doshi; Kanhai Amin; Shin Mei Chan; Manroop Kaur; Simar S. Bajaj; Pavan Khosla; Veer T. Kothari; Ali Mozayan; Irena Tocino; Sophie Chheang (PLoS ONE, 2025)
Radiology reports are an integral part of patient medical records; however, these reports often contain complex medical terminology that are difficult for patients to comprehend, potentially leading to anxiety, misunderstanding, and misinterpretation. The development of user-friendly instruments to improve understanding is thus critically important to enhance health literacy and empower patients. In this study, we introduce a novel artificial intelligence (AI) interface, the Rads-Lit Tool, which can simplify radiology reports for patients using natural language processing (NLP) techniques. This manuscript presents the development process, methodology, and results of the Rads-Lit Tool, demonstrating its potential to simplify radiology reports across various examination types and complexity levels. Our findings highlight that patient-facing AI-driven tools can enhance patient health literacy and foster improved patient-provider communication in radiology.