North America Artificial Intelligence (AI) in Diagnostics Market Report 2022 to 2030

North America Artificial Intelligence (AI) in Diagnostics Market Analysis

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This report presents a strategic analysis of the North America Artificial Intelligence (AI) in Diagnostics Market and a forecast for its development in the medium and long term. It provides a broad overview of the market dynamics, trends and insights, growth drivers and restraints, segmentation, competitive landscape, healthcare policies and regulatory framework, reimbursement scenario, challenges and future outlook. This is one of the most comprehensive reports about the North America Artificial Intelligence (AI) in Diagnostics Market, offering unmatched value, accuracy and expert insights.

ID: IN10NADH002 CATEGORY: Digital Health GEOGRAPHY: North America

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North America Artificial Intelligence (AI) in Diagnostics Market Executive Summary

North America's fastest-growing economy in 2021 was the United States. United States health spending is projected to have grown by 4.2% to $4.3 trillion in 2021, compared with a growth surge of 9.7% in 2020, according to the report from actuaries at the Centers for Medicare and Medicaid Services.

Total health spending in Canada reached a new level in 2021, at more than $308 billion, or $8,019 per Canadian. Health expenditure represented around 12.7% of Canada’s gross domestic product (GDP) in 2021, following a high of 13.7% in 2020. Governments and insurance stakeholders also need to discuss win-win reimbursement schemes that allow the democratization of telemedicine and provide lasting benefits for patients at an effective cost.

Digital healthcare is developing globally as a result of IT technology development and the emergence of remote patient monitoring (RPM) solutions. RPM has developed into a useful tool for enhancing clinical assessment and decision-making while lowering the likelihood of hospitalization. With the prevalence of cancer, diabetes and cardiovascular diseases rising, RPM service use is also anticipated to rise. Additionally, as the aging population grows, more of them are choosing independent, healthy lifestyles, which will probably increase the adoption of RPM solutions.

AI in medical diagnostics includes AI solutions that aid physicians with medical decision-making. AI-based solutions can be used to diagnose cancer, triage critical findings in medical imaging, flag acute abnormalities, help radiologists prioritize life-threatening cases, diagnose cardiac arrhythmias, predict stroke outcomes, and help with the management of chronic diseases.

Market Size and Key Findings

The Global Artificial Intelligence (AI) in Diagnostics market is estimated to be valued at US$ 17.83 Bn in 2021 and is expected to exhibit a CAGR of 20.69% over the forecast period (2021-2030).

North America Artificial Intelligence (AI) in Diagnostics Market Size (In USD Bn) (2021-2030F)

Market Dynamics

Market Growth Drivers Analysis

Large and complicated data are produced at various stages of the care delivery process as a result of the growing digitalization and use of information technologies in the healthcare sector. In the field of medical diagnostics, big data includes, among other things, the information generated by clickstream and online & social media interactions, as well as readings from medical equipment including sensors, ECGs, X-rays, and other imaging devices, as well as medical claims and other billing records. It also includes biometric data. With the increasing acceptance of EHRs, digital laboratory slides, and high-resolution radiological images among healthcare practitioners over the past ten years, big data and analytical solutions have become exponentially more sophisticated and widely used. Particularly in the US, one of the top five big data businesses in healthcare. The usage of bidirectional patient portals, which enable patients to upload data and photos to their EMRs, is anticipated to result in a rise in the volume of big data in medical diagnostics in the upcoming years

Market Restraints

With the use of AI technology, doctors can better diagnose and treat patients. However, it has been noted that doctors are reluctant to adopt new technology. For instance, doctors mistakenly believe that AI would eventually supplant them in the medical field. Because empathy and persuasion are considered to be human abilities, doctors and radiologists think that the use of technology cannot entirely replace the need for a physician. It's also feared that patients may have an unhealthy preference for these technologies and avoid critical in-person treatments, which might put long-term doctor-patient relationships at risk. Many medical practitioners are skeptical of AI's ability to correctly diagnose patient problems.

As a result, it is difficult to persuade providers that AI-based solutions are economical, effective, and secure options that provide doctors with convenience and improved patient care. Healthcare organizations are, however, more open to the potential advantages of AI-based solutions and the variety of fields they may be used in. As a result, it's possible that in the years to come, physicians and radiologists may be more receptive to AI-based healthcare technology.

Competitive Landscape

Key Players

The prominent players operating in this market include Aidoc; AliveCor, Inc.; Vuni, Inc.; Digital Diagnostics, Inc.; Siemens Healthineers; Neural Analytics; Riverain Technologies; Zebra Medical Vision, Inc; GE Healthcare; Imagen Technologies

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.