In biopharma and healthcare research, the abundance of artificial intelligence (AI) algorithms and growing computing power has brought disruption to the industry. This led to an increase in multi-modal data and the analysis of this data can reveal insights that were previously unimaginable. Integrating these diverse data types into a cohesive, actionable format is a major bottleneck in biopharma and clinical research. Every type of data comes with its set of challenges, for example, clinical data is mostly unstructured, omics data tends to be massive and complex, and imaging data requires specialized processing. To add to this complexity, we face issues like inconsistent formats, missing metadata, and the need to ensure data interoperability. These challenges slow down the journey from data to insights, making it difficult to leverage the full potential of multi-modal analysis.
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