EU AI in Healthcare Market Analysis


  • Type: Service area
  • Published : June 2021
  • Region: Europe
  • Country: Other
  • Report ID : 4881

  • Format: PPT, PDF

EU AI in Healthcare Market: Segmented by Product (AI Integrated Hardware, Software and Services), by Technology (Machine Learning, Natural Language Processing, Context Aware Computing and Computer Vision), by End Use Application (Patient data and risk analysis, Inpatient care and hospital management, Medical imaging and diagnostics, Lifestyle management and remote patient monitoring, Virtual Assistants, Drug discovery, Research, Healthcare Assistance Robots, Precision medicine, Emergency room and surgery, Wearables, Mental Health and Cybersecurity) and by End User (Hospitals and healthcare providers, patients, Pharmaceuticals and Biotechnological companies, Healthcare payers and others)- Growth, Trends, and Forecasts (2020 – 2028)

Published Date: June, 2021


SKU: 4881 Categories: ,

Report Overview

The EU AI in Healthcare market size was valued at USD 2.04 billion in 2020 and is forecasted to reach the market size of USD 50.42 billion by 2028, expanding at a compound annual growth rate (CAGR) of 49.3% during the forecast period.

The AI in healthcare market in Europe region has occupied the second highest position in the global scenario. Healthcare systems across the EU face increasing demand as longevity increases, with one in four people in Europe estimated to be over the age of 65 by 2050. At the EU level, a more robust data infrastructure us being pushed to ensure that the healthcare systems can share data which would enable tracking and diagnosing diseases quickly and efficiently.

AI has various applications in healthcare. It enables medical practitioners to retrieve information, interpret images acquired during scans and make treatment plans for the medical procedures.

In addition to the clinical aspect, Artificial Intelligence is also proving beneficial in research activities. European Government and private organizations are initiating the incorporation of Artificial Intelligence in their research endeavours to accelerate developments surrounding COVID-19 vaccines. The COVID-19 outbreak has significantly promoted the implementation of remote health check-ups using digital tools, delivering clinical services to patients over distance rather than in-person.

Market Drivers

The market is driven by the increasing influx of large and complex healthcare datasets for which Artificial Intelligence applications are required.

In addition to this, there is a rise in investment in AI Healthcare Start-ups in the European region. Escalating cross-industrial partnerships and collaborations in the field of AI in healthcare also propels the market towards growth.

In addition to this, the market is fuelled by growing government initiatives to promote implementation of AI in Healthcare.

Increase in use of precision medicines and clinical trial processes demands the deployment of Artificial Intelligence which further propels the market. Increased funding for Research and Development in the field contributes to the growth of the market. It is also driven by the ongoing COVID-19 pandemic.

Type of Artificial Intelligence in Healthcare

The AI technologies that play an important role in healthcare are as follows:

  1. Machine learning – neural networks and deep learning
  2. Natural language processing
  3. Rule-based expert systems
  4. Physical robots
  5. Robotic process automation

The most common form of AI is Machine Learning which is a statistical technique for fitting models to data and to learn by training models with data.


1. EU AI in Healthcare Market Overview………………………………………………………
A. Market Size
2. Market Growth Drivers ………………………………………………………………….
A. Influx of large and complex healthcare datasets
B. Rise in AI Healthcare start-ups
C. Escalating cross-industrial partnerships and collaborations
D. Growing Government initiatives
E. Increase in use of Precision Medicine and clinical trials
F. Increased R&D Funding
G. COVID-19 Pandemic
3. Types of AI related to healthcare………………………………………………………………
A. Machine learning – neural networks and deep learning
B. Natural language processing
C. Rule-based expert systems
D. Physical robots
E. Robotic process automation
4. EU AI in Healthcare Market Segmentation……………………………………………..
A. Product
I. AI Integrated Hardware
II. Software
III. Services
B. Technology
I. Machine Learning
II. Natural Language Processing
III. Context Aware Computing
IV. Computer Vision
C. End Use Application
I. Patient data and risk analysis
II. Inpatient care and hospital management
III. Medical imaging and diagnostics
IV. Lifestyle management and remote patient monitoring
V. Virtual Assistants
VI. Drug discovery
VII. Research
VIII. Healthcare Assistance Robots
IX. Precision medicine
X. Emergency room and surgery
XI. Wearables
XII. Mental Health
XIII. Cybersecurity
D. End User
I. Hospitals and healthcare providers
II. Patients
III. Pharmaceuticals and Biotechnological companies
IV. Healthcare Payers
V. Others
5. AI Major Products Market Share…………………………………………………………
A. Market Analysis, Insights and Forecast – By Revenue
6. Competitive Landscape…………………………………………………………………
A. Major Players
7. Key Company Profiles…………………………………………………………………..
8. Healthcare Policies and Regulatory Landscape…………………………………
A. Policy changes and Reimbursement scenario
9. Factors Driving Future Growth……………………………………………………….
A. New Trends and Development of EU AI in Healthcare market
B. Future Opportunities
10. Conclusion……………………………………………………………………………………………….

Market Segmentation

By Product:

EU AI in Healthcare market is segmented into AI integrated Hardware, Software and Services.

  • AI integrated Hardware includes Processor, Memory and Network.
  • Software includes on-premises and cloud solutions along with an AI platform which houses the machine learning framework and the Application Program Interface.
  • Services include Deployment and Integration of AI hardware and software solutions, Support and Maintenance.

By technology:

Based on the technology used, the EU AI in Healthcare market is segmented into Machine Learning, Natural Language Processing, Context Aware Computing and Computer Vision.

  • Machine Learning has further types like Deep Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning and others. Imaging and diagnostics, and drug discovery are among the applications that use deep learning. It is also used to automate data analysis to deal with large volumes of data.
  • Natural Language Processing is widely used by clinical and research community in healthcare to develop and manage semi-structured and unstructured textual documents, such as electronics health reports, pathology reports, and clinical notes. The demand has risen with increase in number of healthcare institutions structuring and interpreting their data more accurately.
  • Context Aware Computing has seen a boom after the development of sophisticated hard and soft sensors.
  • Computer Vision has been responsible for significant contributions to the fields of surgery and therapy. This offers precise diagnoses, minimising false positives. Computer vision algorithms are trained to utilise huge amounts of data to detect the presence of a condition which the human doctors might miss owing to their sensory limitations.

By End-Use Application:

Based on the end use application, the market is segmented into patient data and risk analysis, Inpatient care and hospital management, medical imaging and diagnostics, Lifestyle management and remote patient monitoring, Virtual Assistants, Drug discovery, Research, Healthcare Assistance Robots, Precision medicine, Emergency room and surgery, Wearables, Mental Health and Cybersecurity.

  • Patient risk algorithms consider several variables and express the results as the percentage risk of developing a major fatal or nonfatal disease in the coming years. They offer solutions that can provide predictive insights into patient health using machine learning and natural language processing algorithms.
  • AI and machine-learning methods have the potential to improve the quality and lower the cost of patient care through Clinical Decision Support Systems.
  • Healthcare AI start-ups work in the field of Imaging and Diagnostics generating a large volume of challenging data which is simplified with extraction of insights using machine learning.
  • Babylon Health, a UK-based AI chatbot, can competently read, understand, and learn from anonymized, aggregated, and complied medical datasets, patient health records, and the conference notes that clinicians make.
  • NVIDIA’s collaboration with Paige.AI, which uses AI for cancer pathology and Google’s team up with Suki, an AI-powered voice-enabled digital assistant for doctors have revolutionised healthcare in EU.

By End User:

Based on the end user, the EU AI in Healthcare market is segmented into the following:

  • Hospitals and healthcare providers, where AI is used to predict and prevent readmissions and improve operations of hospitals
  • Patients, where smartphone applications and wearables are targeted to promote the adoption of AI.
  • Pharmaceuticals and Biotechnological companies, where drug discovery, precision medicine and research are targeted to drive the use of AI.
  • Healthcare Payers, where AI is promoted for use in managing risks, identifying claims, trends and for maximising payment accuracy.
  • Others that include patient data, risk analysis etc. 

    Top 5 Major Players

    IBM Corporation, NVIDIA Corporation, Nuance Communications, Microsoft and Intel Corporation are the major players in the Global AI market.

    At AMIA 2020, IBM showcased its work related to AI in Healthcare through injection of clinical knowledge and by incorporating an adaptive healthcare system. One such example is Watson for Drug Discovery from IBM Watson Health is a platform that mines text and data in medical literature to identify, assess, and rigorously formalize the relationships among genes, diseases, and drugs to help researchers uncover potential new therapies and find new uses for existing medicines.

    NVIDIA develops GPUs and delivers value to its consumers through PC, mobile, and cloud architecture. NVIDIA’s partnership with GSK’s AI powered lab for the discovery of new medicines and vaccines is done on the basis of its expertise in GPU optimization and high-performance computational pipeline development along with its significant contributions to the field through NVIDIA Clara Discovery, NVIDIA DGX A100 systems and so on.

    Nuance Communications and Microsoft are focused on transforming patient care with AI powered solutions for physicians, radiologists and hospitals.  Microsoft is a global leader offering intelligent tools and solutions that empower clinical and operational efficiency and save costs. Azure Synapse Analytics, Azure Health GitHub Repo, Microsoft Healthcare bot, Azure API for FHIR, Azure AI and ML and Azure IoT for Healthcare are scalable platforms that bring conversational AI to healthcare.

    Intel Corporation has contributed its significant share to the field of AI by transforming frontline medical care and improving patient experience for a few of their clients.  Intel’s technology innovations are enabling the healthcare sector with data-driven insights and artificial intelligence by introducing various tools and solutions to simplify AI deployment in healthcare.

    Healthcare Policies and Regulatory Landscape

    Recent times have witnessed numerous advancements in the field of healthcare dedicating their successes to Artificial Intelligence. In the EU, there has been requirements and guidance for software manufacturers to demonstrate compliance to medical device regulations and to place their products on the market.

    In Europe, the Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR) include several generic requirements that can apply to software. In addition to this, the devices should comply to avoidance of negative interactions between software and the IT environment, and requirements for electronic programmable systems. Recently, the European Union has proposed a legal framework which addresses potential artificial intelligence risks and seeks to regulate the technology and issue fines for non-compliance that could total billions of dollars.



Research Methodology

Insights10 will provide you reports within 10 key parameters which are:

  1. Market overview
  2. Market growth drivers & restraints
  3. Epidemiology or disease type
  4. Market segmentation
  5. Market share
  6. Competitive landscape
  7. Key company profiles
  8. Healthcare policies & regulatory framework
  9. Reimbursement scenario
  10. Factors driving future growth

Based on our many years of experience, we believe that these are the parameters that are critical to decision making for business stakeholders. Our focused approach to develop reports focused on 10 key parameters, enabled us to arrive at the name “Insights10”.

Our analysis methodology involves three critical stages:

Stage I: Market Data Collection

Primary Interviews: We have developed a network of experts, freelancers and researchers across countries through which we engage with local experts to gather key data points and assumptions around each market. We also engage regularly with some of the best market research agencies such as Atheneum, GuidePoint, GLG, etc. to conduct surveys and interviews, and build intelligence. We have language translators as a part of our team, who between them can cover 30+ languages allowing us to extract better local insights.

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Market Trends: We generally look at macro parameter and micro indicators. The macro parameters includes changes in government policies, demand and supply of the market, government intervention programs and major market share. The micro indicators GDP growth, market size, market volume etc. We also understand nuances specific to each country like US, Canada, India, Germany, etc. and have worked across 60+ countries and hence no only understand global trends, but how these differ by country, how payment models, market structure, cultural parameters, etc. differ in each country.

Market Sizing and Analysis: Our expert data analytics team has created various market forecast models by employing the top-down approach i.e. start with large overall market and segment different areas and bottom-up approach i.e. start with population and epidemiology and roll up based on spend, etc., estimating the size of the market, and distributing among the geographic and/or product segments.

The top-down approach mainly used for new product forecasting and bottom-up approach is used for demand estimation of any product for different countries summed up to form total market. We are able to round off insights and build stronger forecasts because we always do both these methods and triangulate the final numbers.

The study on the market covers the analysis of the leading geographies such as North America, Europe, Asia-Pacific, and Africa for the period of 2020 to 2028. The qualitative analysis covers the industry landscape and trends, market opportunities, competitive landscape and policy and regulatory scenario and the quantitative analysis covers different market estimates and forecasts.

Data Triangulation & Validation:

Data triangulation of various sources and results of the research are carried out by benchmarking with reliable sources such as industry statistics, statistical databases and the company level averages etc.

We make sure to finalize the numbers in alignment to the market research. Firstly, our internal experts ensure thorough validation and checking to ensure the accurate and precise analysis and then validation is also done using multiple data analysis model.  Two level validation is done and entails finalization of report scope and to way of representation pattern.

Stage III: Interpretation and Presentation

Analysis & Interpretation: The information gathered is then analysed and synthesized. A second series of interviews are done if necessary to check and validate. The future opportunities are analysed by understanding product commercialization and many other factors. It also comprises the analysis of data discrepancies observed across various data sources. Information procured from secondary and primary results are then, interpreted by considering following parameters: (a partial list)

  • Establishing market drivers and trends
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  • Market Segment based analysis to obtain revenue / volume
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Data curated from the analysis and interpretation are drawn to portray all in one consolidated report.

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The foreign exchange rates are calculated on the respective dates and for the respective regions covered in the study.

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