US Artificial Intelligence (AI) in Medical Imaging Market Report 2022 to 2030

US Artificial Intelligence (AI) in Medical Imaging Market Analysis

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The US Artificial Intelligence (AI) in Medical Imaging market size was valued at $630 Mn in 2022 and is estimated to expand at a compound annual growth rate (CAGR) of 33.4% from 2022 to 2030 and will reach $6318 Mn. The market is segmented by AI technology, solution, modality, application, and end user. The US Artificial Intelligence (AI) in the Medical Imaging market will grow due to Increasing demand for remote radiology services. Some of the key players in this market are Microsoft (US), NVIDIA (US), IBM (US), Intel Corporation (US), Google, Inc. (Subsidiary of Alphabet, Inc) (US), GE Healthcare (US), Digital Diagnostics, Inc (US), Xilinx (US), InformAI LLC (US), HeartFlow, Inc (US), Enlitic, Inc (US), Day Zero Diagnostics, Inc(US), Butterfly Network, Inc. (US), and others.

ID: IN10USDH023 CATEGORY: Digital Health GEOGRAPHY: US AUTHOR: Chandani Patel

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US Artificial Intelligence (AI) in Medical Imaging Market Executive Summary

The US Artificial Intelligence (AI) in Medical Imaging market size was valued at $630 Mn in 2022 and is estimated to expand at a compound annual growth rate (CAGR) of 33.4% from 2022 to 2030 and will reach $ 6318. Healthcare spending accounted for 17.7% of the US GDP, totaling $3.8 trillion. Medical imaging is a significant contributor to healthcare spending, with imaging tests accounting for a substantial portion of medical expenses. The use of AI in medical imaging has the potential to improve the efficiency and accuracy of imaging tests, leading to cost savings and improved patient outcomes. For example, AI algorithms can be used to detect abnormalities in medical images more accurately and efficiently than human radiologists, which can reduce the need for follow-up tests and imaging.

Additionally, AI-based solutions can help healthcare providers to develop personalized treatment plans for patients, which can improve outcomes and reduce healthcare costs in the long term. For example, AI algorithms can analyze medical imaging data and predict how patients will respond to different treatments, allowing providers to develop treatment plans that are tailored to each patient's individual needs.

The use of AI in medical diagnostics is growing quickly as a result of factors such as growing government initiatives to promote the use of AI-based technologies, increasing adoption of AI solutions by radiologists to relieve work pressure, the influx of big data, the availability of funding for AI-based startups, and the rise in cross-industry partnerships and collaborations. However, issues like a lack of skilled AI workers, hazy regulations, and a resistance among medical experts to accept these solutions are projected to hinder market growth.

The true goals during the development of AI technologies were to make them human-aware, or to create models with human-like thinking traits. Yet, the development of interactive and scalable AI computers continues to be difficult. However, the increase in human interference with AI approaches and interest in learning more about machine learning have created new research obstacles, such as those related to interpretation and presentation, issues with automated components, and intelligent crowdsourcing control. Interpretation difficulties include difficulties AI systems have comprehending precise instructions and knowledge from humans. Delivering the output and feedback methods of the AI system are among the presentation obstacles. Consequently, the greatest opportunity for AI engineers is to create human-aware AI systems.

In 2021, North America accounted for the largest market share. However, the Asia Pacific market is projected to register the highest CAGR of 42.6% during the forecast period. The high growth rate of the Asia Pacific market can primarily be attributed to the growth strategies adopted by key players in emerging markets, the digitization of medical diagnostics infrastructure, the rising geriatric population, the rise in the prevalence of cancer, and the implementation of favorable government initiatives. The use of AI in medical imaging has the potential to impact healthcare expenditure in the US in the future. Hence the market will grow during the forecast period across the nation.

US AI in-medical imaging market report 2022 to 2030

Market Dynamics

Market Growth Drivers

The application of artificial intelligence (AI) to medical imaging is one of the most promising health and medical innovation areas. AI is used in medical imaging in many ways, such as picture collection, processing for assisted reporting, scheduling follow-up visits, data storage, data mining, and more. In recent years, AI has shown outstanding sensitivity and precision in classifying imaging abnormalities, and it is certain to improve tissue-based detection and characterization. Machine learning (ML), a subfield of artificial intelligence, makes use of computational models and methods that resemble the organic neural networks of the brain. The structure of neural networks is composed of layers of connected nodes. Each network node weighs and summarises the input data before being sent to the activation function.

The application of AI in healthcare and medical imaging altered the diagnostic process, which fueled the expansion of the market for AI in medical imaging on a global scale. Artificial intelligence aids medical professionals in the execution of the picture acquisition process as well as in the analysis of these images for the diagnosis and individualized treatment of each patient. Researchers have used AI to automatically recognize challenging patterns in imaging data and objectively assess radiographic features. Artificial intelligence has been applied in radiation oncology to improve a variety of unique picture modalities that are used at different phases of the therapy. Radiation omics, which comprises the high-throughput extraction of a dataset from the field of medical imaging, is one of the most popular research topics today.

Also, AI is essential for the examination of a significant number of medical images, which uncovers sickness symptoms that would otherwise go undetected. As a result, it is projected that during the coming years, the market for AI in medical imaging will grow. Scientists have made great progress in the fight against COVID-19, and new research findings, like analytical studies and publications from academic and industrial researchers, are appearing every day. In the context of medical imaging investigations, an increasing number of researchers are using artificial intelligence (AI) to identify and anticipate diseases. Hence, image analysis, image segmentation of infected lung areas, and analytics for clinical evaluation form the backbone of AI-based COVID-19 diagnosis methods. These AI-based techniques have demonstrated excellent commercialization potential for AI in the field of medical imaging during the predicted timeframe.

Market restraints:

Because of the rapid development of digital health, healthcare practitioners may now assist patients through cutting-edge treatment modalities. Doctors can more accurately identify and treat patients with the use of AI technology. Doctors, it has been noticed, are hesitant to use new technology. For example, doctors erroneously think AI will someday replace them in the medical industry. According to doctors and radiologists, since attributes like empathy and persuasion are seen as being human, technology cannot completely rule out the existence of a doctor. Another concern is that patients' excessive reliance on these technologies may cause them to reject necessary in-person therapies, straining long-term doctor-patient relationships.

Several doctors are concerned about the precision with which AI can identify patients' illnesses. It might be challenging to convince providers that AI-based solutions are cost-efficient, secure, and efficient options that give doctors convenience and improve patient care. Yet, healthcare practitioners are more receptive to the potential benefits of AI-based solutions and the range of industries they may be applied to. As a result, it's feasible that in the years to come, doctors and radiologists will be more open to AI-based healthcare technologies.

Competitive Landscape

Key Players

Microsoft (USA)
NVIDIA (USA)
IBM (USA)
Intel Corporation (USA)
Google, Inc.(Subsidiary of Alphabet, Inc) (USA)
GE Healthcare (USA)
Digital Diagnostics, Inc (USA)
Xilinx (US), InformAI LLC (USA)
HeartFlow, Inc (USA)
Enlitic, Inc (USA)
Day Zero Diagnostics, Inc (USA)
Aidence (Netherlands)
Butterfly Network, Inc. (USA)
Prognos Health (USA)
Viz.ai, Inc (USA)
EchoNous Inc. (USA)
VUNO Inc

Recent Developments

June 2021: FDA accepted AI's application for CINA-LVO, CINA-ICH, and neurovascular crises, according to a report from the Nuance AI marketplace. This is the largest and first gateway of its sort in the United States, providing a single point of entry to a broad range of AI diagnostic models within the radiology reporting platform.

June 2021: VUNO Inc., a South Korean AI business, announced a strategic partnership with Samsung Electronics for the incorporation of the AI-powered mobile digital X-ray system VUNO Med-Chest X-ray within the GM85. This partnership is projected to bring VUNO closer to the expansion of AI applications that are market-ready due to its access to the global market.

Healthcare Policies and Regulatory Landscape

The US did not have specific regulations on the use of artificial intelligence (AI) in medical imaging. However, the Food and Drug Administration (FDA) has issued guidance on the development and validation of AI in medical devices.

The FDA issued a draft guidance in 2019 titled "Clinical Decision Support Software" to provide a framework for regulating AI-based software as a medical device. The guidance sets out the agency's approach to regulating software that analyzes medical images, including requirements for data integrity, clinical evaluation, and quality control.

The FDA has also established the Digital Health Center of Excellence to provide support and guidance to developers of digital health technologies, including AI-based medical imaging software. The center aims to promote innovation while ensuring patient safety and regulatory compliance. Overall, while there are no specific regulations on AI in medical imaging, the FDA's guidance provides a framework for developers to navigate regulatory requirements and ensure their products are safe and effective. It's important to note that the regulatory landscape is constantly evolving, and developers should stay up to date on any changes in regulations that may impact their products.

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

US Artificial Intelligence (AI) in Medical Imaging Market Segmentation

By AI Technology

Deep Learning
Natural Language Processing (NLP)
Others

By Solution

Software Tools/ Platform
Services
- Integration
- Deployment

By Modality

When compared to CT scans, magnetic resonance imaging can produce pictures that are free of imperfections. Due to its efficiency in obtaining details and better-quality pictures of soft tissues, the MRI is frequently seen as a superior alternative to X-rays. Utilizing optical coherence tomography, three-dimensional interactions between the retina and membranes are made possible in order to control the vitreoretinal disease.

CT Scan
MRI
X-rays
Ultrasound Imaging
Nuclear Imaging

By Application

The market is dominated by the digital pathology segment, which can be linked to pathologists' rising productivity. A validation tool for image analytics is provided by digital pathology, helping pathologists process more slides in less time. This facilitates early illness identification and quicker therapy initiation. AI and digital pathology also assist doctors in making patient-centered decisions. The oncology market is also expected to grow in popularity as more individuals become aware of cancer and its increased incidence in the public. Personalized therapy is made possible by artificial intelligence algorithms that identify and comprehend the nature of malignancies. The second section focUses on AI-driven diagnostic imaging for the heart, brain, breast, and mouth.

Digital Pathology
Oncology
Cardiovascular
Neurology
Lung (Respiratory System)
Breast (Mammography)
Liver (GI)
Oral Diagnostics
Other

By End Use

The market is dominated by the healthcare sector. This is becaUse hospitals are widely dispersed and accessible; hence, many patients like hospitals. The market for medical imaging AI is also anticipated to benefit from favorable reimbursement regulations. During the anticipated time, diagnostic centers are anticipated to grow in popularity. This may be attributable to elements including rising patient awareness and a desire for diagnostic procedures and tests, all of which are fueling the market's expansion. Due to its ease in providing high-quality medical facilities in remote places, particularly rural ones, the ambulatory category is expected to develop at a quicker CAGR throughout the projection period. The availability of qualified surgeons and a surplAustralia of the necessary equipment are contributing to the expansion of the hospital market. Government assistance in emerging nations is likely to boost hospital infrastructure and technologies throughout the forecast period, which is anticipated to caUse the hospital segment to see growth.

Hospital and Healthcare Providers
Patients
Pharmaceuticals and Biotechnology Companies
Healthcare Payers
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.