English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

During the diagnostic process, clinicians leverage multimodal information,such as chief complaints, medical images, and laboratory-test results.Deep-learning models for aiding diagnosis have yet to meet this requirement.Here we report a Transformer-based representation-learning model as a clinicaldiagnostic aid that processes multimodal input in a unified manner. Rather thanlearning modality-specific features, the model uses embedding layers to convertimages and unstructured and structured text into visual tokens and text tokens,and bidirectional blocks with intramodal and intermodal attention to learn aholistic representation of radiographs, the unstructured chief complaint andclinical history, structured clinical information such as laboratory-testresults and patient demographic information. The unified model outperformed animage-only model and non-unified multimodal diagnosis models in theidentification of pulmonary diseases (by 12% and 9%, respectively) and in theprediction of adverse clinical outcomes in patients with COVID-19 (by 29% and7%, respectively). Leveraging unified multimodal Transformer-based models mayhelp streamline triage of patients and facilitate the clinical decisionprocess.

A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics