How to Label and Analyze Multimodal Medical AI Data

Encord is revolutionizing multimodal data labeling in the medical industry by empowering AI teams to unlock groundbreaking insights and improve patient outcomes. Multimodal datasets involve processing various types of data, such as audio, video, text, and medical imaging within a unified structure. Encord's platform support for document and audio data enables seamless management and labeling of these complex multimodal datasets. Examples of multimodal medical AI data include DICOM files, medical imaging, electronic health records, lab results, genomic data, and textual data from clinical notes and reports. Challenges in integrating multimodal medical data involve synchronizing imaging data with non-imaging data and inconsistency across different data types. Accurate labeling is crucial for developing successful multimodal medical AI systems, as it allows models to identify patterns, make accurate predictions, and generate reliable insights. Encord offers a powerful solution for labeling multimodal medical data by creating custom editor layouts that display files side-by-side in the label editor.