EU AI in Healthcare Market 2023 Share, Size, Key Players, Revenue Analysis | Prudent Markets

The EU AI in Healthcare Market 2023 Report makes available the current and forthcoming technical and financial details of the industry. It is one of the most comprehensive and important additions to the Prudent Markets archive of market research studies. It offers detailed research and analysis of key aspects of the global EU AI in Healthcare market. This report explores all the key factors affecting the growth of the global EU AI in Healthcare market, including demand-supply scenario, pricing structure, profit margins, production, and value chain analysis.

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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:

Machine learning neural networks and deep learning
Natural language processing
Rule-based expert systems
Physical robots
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.
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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.

Highlighted Segments of EU AI in Healthcare Market:

Industry Overview: The first section of the research study covers an overview of the global EU AI in Healthcare Market, market status and prospects, and product range. In addition, it provides highlights of the major segments of the global Market, i.e., region, type, and application segments.

Competitive Analysis: This report illuminates important mergers and acquisitions, business expansion, product or service differences, market concentration, the competitive status of the global EU AI in Healthcare Market, and market size by player.

Company Profiles and Key Data: This section covers companies profiling the major players in the global EU AI in Healthcare Market based on the aforementioned revenue, products, business, and other factors.

Market size by type and application: In addition to providing an in-depth analysis of the industry size by type and application, this section provides research on top end users or consumers and potential applications.

Market Dynamics: This report covers the drivers, trends, and opportunities of the global EU AI in Healthcare Market. This section also includes Porter's analysis of five forces.

Findings and Conclusions: It provides strong recommendations for new as well as established players for securing a position of strength in the global EU AI in Healthcare Market.

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The report covers the competitive analysis of the market. As the demand is driven by a buyer's paying capacity and the rate of item development, the report shows the important regions that will direct growth. This section exclusively shares insight into the budget reports of big-league members of the market helping key players and new entrants understand the potential of investments in the Global EU AI in Healthcare Market. It can be better employed by both traditional and new players in the industry for complete know-how of the market.

Strategic Points Covered in Table of Content of Global EU AI in Healthcare Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the EU AI in Healthcare market
Chapter 2: Exclusive Summary - the basic information of the EU AI in Healthcare Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the EU AI in Healthcare
Chapter 4: Presenting the EU AI in Healthcare Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying the by Type, End User and Region/Country
Chapter 6: Evaluating the leading manufacturers of the EU AI in Healthcare market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by Manufacturers/Company with revenue share and sales by key countries in these various regions
Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

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