AI Augmentation in Molecular Diagnostics

AI Augmentation in 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.

This research covers emerging technologies and trends, challenges, and opportunities across the manufacturing workflow, from upstream (viral vector production) to downstream (viral vector purification), and highlights the importance of analytical tools along the entire value chain. Key developments in upstream processes for viral vector production include advanced transfection agents, novel plasmids, suspension-adapted cell culture, and stable producer cell lines. The research also discusses the general industry shift toward automation, digitization, and advanced analytical processes, including online and in-line analytics and robust real-time analytics which will support progress in process control and optimization and improve overall efficiencies and safety.

As such, the research presents a bird’s eye view of key stakeholders and their innovative platforms and a snapshot of the collaborative ecosystem to understand the CGT (Cell & Gene Therapy) industry’s dynamic and fast-paced nature.