ReXplain: Translating Radiology into Patient-Friendly Video Reports
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913616368762880 |
|---|---|
| author | Luo, Luyang Vairavamurthy, Jenanan Zhang, Xiaoman Kumar, Abhinav Ter-Oganesyan, Ramon R. Schroff, Stuart T. Shilo, Dan Hossain, Rydhwana Moritz, Mike Rajpurkar, Pranav |
| author_facet | Luo, Luyang Vairavamurthy, Jenanan Zhang, Xiaoman Kumar, Abhinav Ter-Oganesyan, Ramon R. Schroff, Stuart T. Shilo, Dan Hossain, Rydhwana Moritz, Mike Rajpurkar, Pranav |
| contents | Radiology reports, designed for efficient communication between medical experts, often remain incomprehensible to patients. This inaccessibility could potentially lead to anxiety, decreased engagement in treatment decisions, and poorer health outcomes, undermining patient-centered care. We present ReXplain (Radiology eXplanation), an innovative AI-driven system that translates radiology findings into patient-friendly video reports. ReXplain uniquely integrates a large language model for medical text simplification and text-anatomy association, an image segmentation model for anatomical region identification, and an avatar generation tool for engaging interface visualization. ReXplain enables producing comprehensive explanations with plain language, highlighted imagery, and 3D organ renderings in the form of video reports. To evaluate the utility of ReXplain-generated explanations, we conducted two rounds of user feedback collection from six board-certified radiologists. The results of this proof-of-concept study indicate that ReXplain could accurately deliver radiological information and effectively simulate one-on-one consultation, shedding light on enhancing patient-centered radiology with potential clinical usage. This work demonstrates a new paradigm in AI-assisted medical communication, potentially improving patient engagement and satisfaction in radiology care, and opens new avenues for research in multimodal medical communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00441 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ReXplain: Translating Radiology into Patient-Friendly Video Reports Luo, Luyang Vairavamurthy, Jenanan Zhang, Xiaoman Kumar, Abhinav Ter-Oganesyan, Ramon R. Schroff, Stuart T. Shilo, Dan Hossain, Rydhwana Moritz, Mike Rajpurkar, Pranav Artificial Intelligence Image and Video Processing Radiology reports, designed for efficient communication between medical experts, often remain incomprehensible to patients. This inaccessibility could potentially lead to anxiety, decreased engagement in treatment decisions, and poorer health outcomes, undermining patient-centered care. We present ReXplain (Radiology eXplanation), an innovative AI-driven system that translates radiology findings into patient-friendly video reports. ReXplain uniquely integrates a large language model for medical text simplification and text-anatomy association, an image segmentation model for anatomical region identification, and an avatar generation tool for engaging interface visualization. ReXplain enables producing comprehensive explanations with plain language, highlighted imagery, and 3D organ renderings in the form of video reports. To evaluate the utility of ReXplain-generated explanations, we conducted two rounds of user feedback collection from six board-certified radiologists. The results of this proof-of-concept study indicate that ReXplain could accurately deliver radiological information and effectively simulate one-on-one consultation, shedding light on enhancing patient-centered radiology with potential clinical usage. This work demonstrates a new paradigm in AI-assisted medical communication, potentially improving patient engagement and satisfaction in radiology care, and opens new avenues for research in multimodal medical communication. |
| title | ReXplain: Translating Radiology into Patient-Friendly Video Reports |
| topic | Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2410.00441 |