Augmenting medical data interpretation with Large Language Models (LLMs): a comparative analysis of patient empowerment, information processing, and technology acceptance

Abstract Background Medical data interpretation traditionally relies on healthcare professionals as intermediaries, which can limit patient autonomy and engagement. Large Language Models (LLMs) present an opportunity to transform this paradigm by enabling direct patient access to AI-generated interpretations; however, comparative research on their effectiveness across different medical data types and communication modalities remains limited. This study explores how direct LLM-augmented interpretation of medical data, in which patients use an AI system to receive real-time explanations of laboratory and radiological results, compares with healthcare professional-led interpretation across different data modalities, with particular attention to patient comprehension, empowerment, and technology acceptance. Methods Using a mixed-methods approach with a within-subjects experimental design, 45 demographically diverse participants experienced six scenarios: blood work and medical imaging interpretations delivered via (1) healthcare professional phone consultation, (2) in-person consultation, or (3) LLM interaction through a custom-configured ChatGPT-4o interface (Medical Explainer AI) designed to provide plain-language explanations of findings, highlight abnormal values, contextualize clinical significance, explain medical terminology, and adapt explanation complexity based on user feedback. Results LLM interaction significantly enhanced diagnostic comprehension (mean difference = 1.3 compared to phone consultation, p