AI Augmentation in Molecular Diagnostics

AI Augmentation in Molecular Diagnostics

AI Applications to improve predictive, diagnostic, and prognostic value of molecular diagnostics

RELEASE DATE
03-Jan-2022
REGION
Global
Research Code: DA35-01-00-00-00
SKU: HC03488-GL-TR_26154
$4,950.00
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HC03488-GL-TR_26154
$4,950.00
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Description

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.

Table of Contents

1.1 Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth

1.2 The Strategic Imperative 8™

1.3 The Impact of the Top Three Strategic Imperatives in AI Augmented Molecular Diagnostics

1.4 Growth Opportunities Fuel the Growth Pipeline Engine™

2.1 AI and its Growing Prevalence in Healthcare Applications

2.2 What is Driving Adoption of AI/ ML in Healthcare?

2.3 Evolution of AI in Diagnostics

2.4 Key Applications of AI Tools in Molecular Diagnostics

2.5 Research Context

2.6 Research Scope—Key Questions Addressed

2.7 Research Methodology

2.8 Key Findings

3.1 Molecular Diagnostics—Challenges and Needs

3.2 How can AI Accentuate Molecular Diagnostics?

3.3 AI Augmentation to Improve Predictive, Diagnostic, and Prognostic Capabilities of Molecular Diagnostics

3.4 Data Science-Technology Architecture in Molecular Diagnostics

3.5 Applications of AI in Molecular Diagnostics

3.6 Challenges in the Adoption of AI in Molecular Diagnostics

4.1 Applications in Infectious Disease Testing

4.2 Key Stakeholders Developing AI-Augmented Infectious Disease Dx

4.3 Infectious Disease Testing—AI and Opportunities

4.4 Applications in Oncology Diagnostics

4.5 Key Stakeholders Developing AI-Augmented Liquid Biopsies

4.6 Applications in Precision Medicine

4.7 Key Stakeholders Developing AI-Augmented Precision Diagnostics

4.8 How can AI Accentuate Molecular Diagnostics?

4.9 Role of AI in Cancer Molecular Diagnostics

4.10 Applications in Rare Disease Testing

5.1 Strategic Collaboration to Advance AI Molecular Dx

5.2 Funding Analysis of AI- and ML-based Molecular Diagnostics Companies, Jan. 2016─Sept. 2021

5.3 Support for Geographic Expansion and Commercialization from Late-stage Private Funding

5.3 Support for Geographic Expansion and Commercialization from Late-stage Private Funding (continued)

5.3 Support for Geographic Expansion and Commercialization from Late-stage Private Funding (continued)

6.1 GRAIL, Inc.

6.2 Diagnostics.ai

6.3 Sophia Genetics

6.4 Elypta AB

6.5 Freenomemultiomics liquid biopsy for early cancer detection

6.6 Cambridge Cancer Genomics (Dante Labs S.r.l.)

6.7 Immunis.ai

6.8 Amwise Diagnostic Pvt. Ltd.

6.9 Billiontoone

7.1 Growth Opportunity 1: Assay-agnostic, Flexible AI Analytics Platform for Molecular Diagnostics Democratization

7.1 Growth Opportunity 1: Assay-agnostic, Flexible AI Analytics Platform for Molecular Diagnostics Democratization (continued)

7.2 Growth Opportunity 2: AI-enabled Large-scale Genomic Surveillance and Convergence of AI and Big Data for Greater Preparedness

7.2 Growth Opportunity 2: AI-enabled Large-scale Genomic Surveillance and Convergence of AI and Big Data for Greater Preparedness (continued)

7.3 Growth Opportunity 3: AI for Maximum Potential of Multimodal Data

7.3 Growth Opportunity 3: AI for Maximum Potential of Multimodal Data (continued)

7.4 Growth Opportunity 4: AI/ML for RNA Sequencing Applications

7.4 Growth Opportunity 4: Apply AI/ML To RNA Sequencing (continued)

8.1 Technology Readiness Levels (TRL): Explanation

9.1 Your Next Steps

9.2 Why Frost, Why Now?

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Related Research
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.
More Information
No Index No
Podcast No
Author Rruplekha Choudhurie
Industries Healthcare
WIP Number DA35-01-00-00-00
Is Prebook No