Performance of large language models for CAD-RADS 2.0 classification derived from cardiac CT reports

Journal of cardiovascular computed tomography, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

The Coronary Artery Disease-Reporting and Data System (CAD-RADS) 2.0 offers standardized guidelines for interpreting coronary artery disease in cardiac CT. Accurate consistent CAD-RADS scoring is crucial comprehensive characterization clinical decision-making. This study investigates the capability of large language models (LLMs) to autonomously generate scores from CT reports. A dataset reports was created evaluate performance several state-of-the-art LLMs generating via in-context learning. tested comprised GPT-3.5, GPT-4o, Mistral 7b, Mixtral 8 ​× ​7b, Llama3 8b, 8b with a 64k context length, 70b. generated each model were compared ground truth, which provided by two board-certified cardiothoracic radiologists consensus based on final set 200 GPT-4o 70b achieved highest accuracy full including all modifiers rate 93 ​% 92.5 ​%, respectively, followed ​7b 78 ​%. In contrast, older LLMs, such as 7b GPT-3.5 performed poorly (16 ​%) demonstrated intermediate results an 41.5 enhanced learning are capable excellent accuracy, potentially enhancing both efficiency consistency reporting. Open-source not only deliver competitive but also present benefit local hosting, mitigating concerns around data security.

Язык: Английский

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Natural Language Processing for Everyone

Radiology Artificial Intelligence, Год журнала: 2025, Номер 7(3)

Опубликована: Апрель 16, 2025

Язык: Английский

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Open source ML framework algorithms for biophysical models

The European Physical Journal Special Topics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

Язык: Английский

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Reasoning large language models in rheumatology: a call for responsible action

The Lancet Rheumatology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

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