UK Artificial Intelligence (AI) in Diagnostics Market Analysis

UK Artificial Intelligence (AI) in Diagnostics Market Analysis


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The UK Artificial Intelligence (AI) in the diagnostics market is projected to grow from $0.03 Bn in 2022 to $0.25 Bn by 2030, registering a CAGR of 33% during the forecast period of 2022 - 2030. The market will be driven by the advancing government initiatives to support the adoption of AI technology & the rising rate of chronic illnesses. The market is segmented by component & by diagnosis. Some of the major players include GE Healthcare, IBM Watson Health & Brainomix Limited.

ID: IN10GBDH002 CATEGORY: Digital Health GEOGRAPHY: UK AUTHOR: Vidhi Upadhyay

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UK Artificial Intelligence (AI) in Diagnostics Market Executive Summary

The UK Artificial Intelligence (AI) in the diagnostics market is projected to grow from $0.03 Bn in 2022 to $0.25 Bn by 2030, registering a CAGR of 33% during the forecast period of 2022 - 2030. Healthcare spending in the United Kingdom will account for 11.9% of the GDP by 2021. Hospital treatments are complimentary for individuals who are "ordinarily resident" in the UK. The National Health Service is a government-sponsored universal healthcare system in the United Kingdom (NHS). The NHS is a collection of publicly financed healthcare services in the United Kingdom. In the UK, around 7.6 Mn individuals have heart and circulatory disorders; an elderly and expanding population, as well as improving survival rates from heart and circulatory events, may cause these numbers to climb even higher.

Medical imaging is a crucial area where AI is being employed in diagnoses. AI systems can examine medical pictures and detect anomalies, allowing clinicians to make a more accurate diagnosis. Another application of AI in diagnostics is the discovery of biomarkers. Large volumes of data may be analyzed by AI to uncover tendencies and biomarkers that can be used to detect disorders. This can assist increase diagnostic accuracy and allow for the early diagnosis of diseases.

December 2022, The Brainomix e-Stroke imaging platform, designed by the UK's med-tech solution business Brainomix, is being utilized to diagnose strokes and identify the appropriate therapy across 11 stroke treatment networks in the UK's state-funded National Health Service (NHS). In November 2022, The University of the West of Scotland developed an AI system that takes X-rays and then compares the scans to a database of thousands of pictures of people with pneumonia, TB, and Covid in order to aid diagnosis. They are now investigating if the technology may be used to detect cancer

uk artificial intelligence in diagnostics market analysis

Market Dynamics

Market Growth Drivers

The NHS Artificial Intelligence Laboratory (NHS AI Lab) was established in August 2019, to address the challenge by bringing together government, health and care providers, academia, and technology businesses. By bringing together programs that address the challenges of creating and implementing AI systems in health and care, the NHS AI Lab fosters cooperation and co-creation. This is expected to unleash AI's ability to revolutionize healthcare. Moreover, the UK also has The National Covid-19 Chest Imaging Database, a consolidated UK imaging collection with approximately 29,000 pictures from 80 UK facilities that are already being used to train algorithms by technology businesses and academic organizations.  Other factors such as increasing need for AI in healthcare, expanding awareness of individualized treatment, and the need for cost-effective and efficient healthcare services. The increasing frequency of chronic diseases such as cancer and heart disease is also pushing the adoption of AI-based diagnostics in the UK.

Market Restraints

There are various constraints that might impede the growth of AI in Diagnostics markets in the UK, such as legal concerns around data privacy and a lack of standardization in the development of AI-based solutions. The high expenses of deploying AI-based solutions, as well as the necessity for specific training for healthcare workers in the usage of these technologies, may be a hurdle for certain healthcare providers.

Competitive Landscape

Key Players

  • GE Healthcare
  • IBM Watson Health
  • Siemens Healthineers
  • Philips Healthcare
  • Google Health
  • AliveCor, Inc. (USA)
  • Riverain Technologies (USA)
  • AstraZeneca
  • Brainomix Limited (GBR)
  • Kheiron Medical Technologies (GBR)
  • Optellum (GBR)

Notable Deals

  1. February 2023, GE HealthCare to Acquire Caption Health The acquisition adds AI-enabled image guiding to the ultrasound device portfolios of GE HealthCare's $3 billion Ultrasound division
  2. January 2023, Portsmouth Hospitals University NHS Trust has increased its symptomatic and breast screening services by installing five Siemens Healthineers MAMMOMAT Revelation mammography devices in community and hospital locations

Healthcare Policies and Regulatory Landscape

The UK's regulatory agency for medications and medical devices, including AI-based medical devices, is the Medicines and Healthcare Products Regulatory Agency (MHRA). Before medical devices may be commercialized in the UK, the MHRA must ensure that they meet the relevant safety and performance criteria.

The Accelerated Access Collaboration (AAC) is a unique collaboration comprised of the following patient organizations, government agencies, businesses, and NHS organizations working together to accelerate the implementation of breakthrough healthcare advances. Within NHS England, the AAC has formed a new specialized unit. National Imaging Academy Wales creates and provides diagnostic imaging training and professional development courses for any diagnostic imaging practitioner, student, or trainee, in partnership with NHS, academia, and/or industry partners.

Reimbursement Scenario

The National Institute for Health and Care Excellence (NICE) evaluates items to see if they are cost-effective and therefore be funded by the NHS. NICE-recommended items are anticipated to be funded by NHS health service providers.

1. Executive Summary
1.1 Digital Health Overview
1.2 Global Scenario
1.3 Country Overview
1.4 Healthcare Scenario in Country
1.5 Digital Health Policy in Country
1.6 Recent Developments in the Country

2. Market Size and Forecasting
2.1 Market Size (With Excel and Methodology)
2.2 Market Segmentation (Check all Segments in Segmentation Section)

3. Market Dynamics
3.1 Market Drivers
3.2 Market Restraints

4. Competitive Landscape
4.1 Major Market Share

4.2 Key Company Profile (Check all Companies in the Summary Section)

4.2.1 Company
4.2.1.1 Overview
4.2.1.2 Product Applications and Services
4.2.1.3 Recent Developments
4.2.1.4 Partnerships Ecosystem
4.2.1.5 Financials (Based on Availability)

5. Reimbursement Scenario
5.1 Reimbursement Regulation
5.2 Reimbursement Process for Diagnosis
5.3 Reimbursement Process for Treatment

6. Methodology and Scope

Artificial Intelligence (AI) in Diagnostics Market Segmentation

  • By Component Outlook Type (Revenue, USD Billion):
    • Software
    • Hardware
    • Services
  • By Diagnosis Outlook Type (Revenue, USD Billion):
    • Cardiology
    • Oncology
    • Pathology 
    • Radiology
    • Chest and Lung
    • Neurology
    • Others

Methodology for Database Creation

Our database offers a comprehensive list of healthcare centers, meticulously curated to provide detailed information on a wide range of specialties and services. It includes top-tier hospitals, clinics, and diagnostic facilities across 30 countries and 24 specialties, ensuring users can find the healthcare services they need.​

Additionally, we provide a comprehensive list of Key Opinion Leaders (KOLs) based on your requirements. Our curated list captures various crucial aspects of the KOLs, offering more than just general information. Whether you're looking to boost brand awareness, drive engagement, or launch a new product, our extensive list of KOLs ensures you have the right experts by your side. Covering 30 countries and 36 specialties, our database guarantees access to the best KOLs in the healthcare industry, supporting strategic decisions and enhancing your initiatives.

How Do We Get It?

Our database is created and maintained through a combination of secondary and primary research methodologies.

1. Secondary Research

With many years of experience in the healthcare field, we have our own rich proprietary data from various past projects. This historical data serves as the foundation for our database. Our continuous process of gathering data involves:

  • Analyzing historical proprietary data collected from multiple projects.
  • Regularly updating our existing data sets with new findings and trends.
  • Ensuring data consistency and accuracy through rigorous validation processes.

With extensive experience in the field, we have developed a proprietary GenAI-based technology that is uniquely tailored to our organization. This advanced technology enables us to scan a wide array of relevant information sources across the internet. Our data-gathering process includes:

  • Searching through academic conferences, published research, citations, and social media platforms
  • Collecting and compiling diverse data to build a comprehensive and detailed database
  • Continuously updating our database with new information to ensure its relevance and accuracy

2. Primary Research

To complement and validate our secondary data, we engage in primary research through local tie-ups and partnerships. This process involves:

  • Collaborating with local healthcare providers, hospitals, and clinics to gather real-time data.
  • Conducting surveys, interviews, and field studies to collect fresh data directly from the source.
  • Continuously refreshing our database to ensure that the information remains current and reliable.
  • Validating secondary data through cross-referencing with primary data to ensure accuracy and relevance.

Combining Secondary and Primary Research

By integrating both secondary and primary research methodologies, we ensure that our database is comprehensive, accurate, and up-to-date. The combined process involves:

  • Merging historical data from secondary research with real-time data from primary research.
  • Conducting thorough data validation and cleansing to remove inconsistencies and errors.
  • Organizing data into a structured format that is easily accessible and usable for various applications.
  • Continuously monitoring and updating the database to reflect the latest developments and trends in the healthcare field.

Through this meticulous process, we create a final database tailored to each region and domain within the healthcare industry. This approach ensures that our clients receive reliable and relevant data, empowering them to make informed decisions and drive innovation in their respective fields.

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Last updated on: 22 February 2023
Updated by: Anish Swaminathan

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