Integration of multimodal large language models into point-of-care ultrasound documentation: A pilot study.

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Abstract

There are no studies evaluating the potential to deploy artificial intelligence in the interpretation of cardiac point-of-care ultrasound (POCUS) documentation. Telexy Health developed an image interpretation and documentation system using multimodal model extensions with video capabilities (MLLM) and deployed it on the POCUS manager QpathE. This study evaluated the MLLM's agreement to experts in identifying cardiac POCUS views and interpreting normal and abnormal examinations. This was a retrospective pilot study using cardiac POCUS studies stored in QPathE. The MLLM was applied to POCUS examinations until 350 examinations were reached, 100 normal, and 250 with pathology. MLLM view identification and interpretation were recorded using Qualtrics, with expert image review serving as the gold standard. The MLLM was applied to 370 examinations, with 20 failures. View identification agreed with expert review in 151 examinations (43.1%). Cohen's kappa showed fair agreement for apical four-chamber views between expert and MLLM, with moderate agreement for parasternal long axis, parasternal short axis, subxiphoid, and inferior vena cava views. The MLLM agreed with expert interpretation 86 of 100 normal examinations; the agreement varied by pathology with pericardial effusion, ejection fraction, aortic outflow tract, chamber size, and inferior vena cava having an agreement of 8%, 4%, 70%, 0%, and 12% respectively. While agreement in image interpretation was poor, this vendor agnostic workflow demonstrated the ability to integrate an MLLM model into image interpretation. While it should not be incorporated into clinical practice in this state, future model improvement may enable automated POCUS interpretation and documentation.

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