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 < 0.001), reduced cognitive load, increased perceived control, and improved time efficiency. Healthcare professional-led interpretation, particularly in-person, maintained advantages in fostering trust, reducing anxiety, and enhancing confidence in decision-making. The benefits of LLM interaction were more pronounced for blood work than for medical imaging interpretation. Age, education level, and health literacy significantly moderated the effectiveness of different interpretation methods.

Conclusions: LLMs offer complementary rather than replacement capabilities for medical data interpretation, excelling in enhancing comprehension, control, and efficiency, while healthcare professionals provide superior relational value through trust, confidence, and emotional support. Implementation strategies should leverage the strengths of both approaches, carefully considering data complexity and patient characteristics to maximize benefits while ensuring equitable access.