Announcing a new article publication for Cardiovascular Innovations and Applications.  The aim of this study was to assess the feasibility of two large language models (LLMs), GPT-5.2 and DeepSeek-V3.2, for simplifying cardiac magnetic resonance (CMR) reports into participant-accessible language.

Participants undergoing CMR examinations were prospectively recruited. Original reports were randomly assigned in a 1:1 ratio to either GPT-5.2 or DeepSeek-V3.2. Predesigned prompts were used to guide the LLMs in generating simplified reports. Two customized structured Likert-scale questionnaires were developed to assess the performance and comprehensibility of the LLM-generated reports. The internal consistency and factor structure of these questionnaires were evaluated.

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