How is AI Integration Augmenting the Growth of Molecular Diagnostics?
AI applications to improve predictive, diagnostic, and prognostic value of molecular diagnostics
This Frost & Sullivan research service discusses advances in AI integration in Molecular Diagnostics, highlights key advantages of incorporating AI in the diagnostics workflow and roadblocks that have prevented it from becoming mainstream. While AI is widely used in radiology- and pathology-based diagnostics, its adoption is gradually increasing in other modalities, including molecular diagnostics.
Molecular diagnostics are extremely sensitive tests that are paving the way for precision medicine. They can accurately predict disease occurrence, enable early disease detection, and support clinicians in making therapeutic decisions. AI augmentation can greatly improve diagnostic accuracy while making test results easier to interpret. Cloud-hosted, flexible, AI-based analytical systems can be used by a number of laboratories that carry out in-house genomics research. Many industry stakeholders are leveraging innovative, sustainable business models to deploy AI in their diagnostics workflow. In the next few years, AI-based diagnostics will be adopted more widely and in a streamlined manner, to enhance diagnostic performance, and facilitate disease classification and guidance of treatment. Deep learning algorithms work better for the analysis of genomic data, while unsupervised algorithms show promise with limited datasets, and they have been used as a predictive tool for cancer and rare disease prediction. Currently, the highest utilization of AI in molecular diagnostics is in cancer and infectious disease testing.
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