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

The US 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 US AI in Healthcare market. This report explores all the key factors affecting the growth of the global US AI in Healthcare market, including demand-supply scenario, pricing structure, profit margins, production, and value chain analysis.

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Report Overview

The US AI in Healthcare market size was valued at USD 2.40 billion in 2020 and is forecasted to reach the market size of USD 43.40 billion by 2028, expanding at a compound annual growth rate (CAGR) of 43.6% from 2020 to 2028.

Artificial Intelligence can bring about substantial improvements to any process within the healthcare functions of operation and delivery. In January 2018, the University of Massachusetts Amherst Centre for Data Science partnered with Chan Zuckerberg Initiative (CZI) to fast-track healthcare research and development using artificial intelligence. AI is forecasted to cut down US healthcare costs by USD 150 billion in 2026. In the US, the White House partnered with AI research institutions to mine scientific literature to better understand COVID-19.

AI tools are expected to facilitate and enhance human work and not replace the work of human physicians and other healthcare staff. AI can foster preventative medicine and new drug discovery which significantly improves the quality of healthcare. IBM Watsons ability to pinpoint treatments for cancer patients and Googles healthcare app that organizes and stores healthcare data enabling easy access are some of the developments.

Even amid economic uncertainties due to COVID-19, Healthcare AI companies have gained numerous opportunities to thrive upon owing to the reason that they minimize human interaction, thus minimising the risk of transmission and shielding the frontline workers.

Market Drivers

The US AI in Healthcare market is driven by the influx of large and complex healthcare datasets. The increased demand for minimising healthcare expenditure further drivers the market. The need to cut down hardware costs by maximising computing efficiency fuels the market growth.

In addition to this, the market is driven by growing government initiatives and their collaboration with leading AI companies to promote implementation of AI in Healthcare.

There is an imbalance between the healthcare workforce and the patients. Artificial Intelligence restores this balance, further fuelling the market growth. 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:

US 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 US 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. Major start-ups in this field are CureMetrix (California, US), PathAI (Massachusetts, US), Subtle Medical (California, US), Zebra Medical Vision (Israel), Arterys (California, US), Imagen Technologies (New York, US), Viz.AI (California, US), RADLogics (Massachusetts, US), Bay Labs (California, US), Mindshare Medical (Washington, US), Enlitic (California, US), and Proscia (Pennsylvania, US).

Some of the best AI in Health Start-ups in the US are Roam Analytics, Sopris Health, Artelus, Appto Health, Sense.ly and Atomwise.

Roam Analytics builds an AI platform for the healthcare industry that uses Natural Language Processing (NLP) to enhance the structure and content of patients' electronic medical records as well as other AI tools for data analysis and storage.

Sopris Health offers an AI-powered solution that automates the process of collecting, categorizing, analysing and documenting patients' symptoms, clinical history and feedback.

Artelus develops an AI-powered solution that can detect diabetic retinopathy (DR) with high accuracy in under three minutes.

Appto Health and its AI platform facilitate front-office automation for clinics and healthcare providers, automating patient scheduling, gathering patient feedback and answering organizational questions and more via their phone and messaging AI-powered bots.

ly and their AI-based virtual nurse Molly helps with prolonged patient monitoring, gathering feedback and symptoms from an individual by using NLP algorithms.

Atomwise employs the above-mentioned approach to analyse existing medications data and study their contents in order to come up with ways of modifying them to create new types of drugs that would be able to cure a given disease.

By End User:

Based on the end user, the US 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. Yammer (US), StorSimple (US) and Xamarin (US) are among the subsidiaries of Microsoft in the United States.

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 last ten years, 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 the US, The FDA published a paper for a proposed regulatory framework for modifications to AI or Machine Learning based applications based on current FDA premarket programs, De Novo and Premarket Approval pathways.
Highlighted Segments of US AI in Healthcare Market:

Industry Overview: The first section of the research study covers an overview of the global US 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 US 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 US 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 US 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 US 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 US 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 US AI in Healthcare Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the US AI in Healthcare market
Chapter 2: Exclusive Summary - the basic information of the US AI in Healthcare Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the US AI in Healthcare
Chapter 4: Presenting the US 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 US 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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