|
Forecast
Period
|
2026-2030
|
|
Market
Size (2024)
|
USD
1.65 Billion
|
|
Market
Size (2030)
|
USD
4.36 Billion
|
|
CAGR
(2025-2030)
|
17.53%
|
|
Fastest
Growing Segment
|
Deep
Learning
|
|
Largest
Market
|
North
America
|
Market Overview
Global AI In Medical Imaging Market was valued at USD
1.65 Billion in 2024 and is expected to reach USD 4.36 Billion by 2030 with a
CAGR of 17.53% during the forecast period. The Global AI in Medical
Imaging Market is driven by several key factors, including the increasing
demand for improved diagnostic accuracy, the growing volume of medical imaging
data, and the need for faster diagnoses. AI technologies, such as machine
learning and deep learning, enable more efficient image analysis, enhancing the
ability to detect and diagnose diseases like cancer, cardiovascular conditions,
and neurological disorders at earlier stages. The rise of healthcare
digitalization and the integration of electronic health records (EHRs) have
accelerated the adoption of AI tools in medical imaging. The shortage of radiologists
and healthcare professionals, coupled with the desire for cost-effective
solutions and increased healthcare efficiency, further boosts AI adoption in
medical imaging, ensuring quicker, more accurate, and scalable results.
Key Market Drivers
Increasing Demand for Diagnostic Accuracy and Early
Disease Detection
The rising demand for
diagnostic accuracy and early disease detection is a key factor driving the
adoption of AI in medical imaging. As healthcare systems around the world face
mounting pressure to improve patient outcomes and reduce diagnostic errors, AI
technology provides a solution that can significantly enhance the precision of
medical diagnoses. Traditional diagnostic imaging, although highly effective,
can still be subject to human error, fatigue, and subjectivity. Radiologists
may occasionally miss subtle abnormalities in medical images, leading to
delayed or incorrect diagnoses. In October 2023, Koninklijke Philips N.V.
launched the Philips Image Guided Therapy Mobile C-arm System 3000 (Zenition
30), a new X-ray system that provides real-time image guidance for a wide range
of clinical procedures. These include orthopedics, trauma, spine interventions,
pain management, and surgical operations, all tailored for use in operating
rooms.
AI, particularly deep learning algorithms, is
designed to detect patterns and anomalies in medical images that are often
invisible to the human eye. These algorithms have been trained on vast datasets
of medical images and continuously learn to identify critical issues such as
tumors, lesions, and other abnormalities, even in early stages of development.
Early detection of diseases like cancer, cardiovascular diseases, and
neurological disorders is crucial in improving patient survival rates. AI tools
are also beneficial in screening populations at risk, enabling healthcare
providers to intervene early in patients' conditions and prevent the
progression of diseases. AI’s ability to process medical images quickly and
with high precision can expedite the diagnostic process. Faster diagnosis
allows for timely intervention, leading to better clinical outcomes. This
increased focus on early detection and accuracy in diagnosis is one of the main
drivers propelling the growth of the Global AI in medical imaging market, as
healthcare providers recognize the value of these technologies in improving
patient care.
Growing Volume of Medical Imaging Data
The ever-increasing volume of medical imaging data
is a central driver in the global AI in medical imaging market. As medical
technologies improve, imaging devices such as MRIs, CT scanners, X-rays, and
ultrasounds are capable of capturing higher-resolution images and generating
large quantities of data. This increase in data volume presents a significant
challenge for healthcare professionals, particularly radiologists, who must
analyze and interpret complex imaging data in a timely and accurate manner.
The human brain can only process a limited amount
of information at any given time, and radiologists often face time constraints
due to the large number of images they need to review daily. Manual image
interpretation is not only time-consuming but is also subject to error,
particularly when there is a high volume of cases to handle. AI is
well-equipped to address these challenges by providing automated solutions for
data processing, image analysis, and interpretation. Machine learning
algorithms can sift through vast amounts of imaging data, automatically
detecting abnormalities and prioritizing cases based on urgency.
By leveraging AI technologies, healthcare providers
can improve the efficiency of image analysis, reduce the workload on
radiologists, and ensure faster and more accurate diagnoses. As the volume of
medical imaging data continues to grow, the need for AI solutions that can
handle and analyze this data efficiently will continue to drive the market's
expansion.
Cost Reduction and Operational Efficiency
The rising need for cost reduction and operational
efficiency in healthcare systems is also a key factor driving the growth of AI
in medical imaging. Healthcare costs continue to rise globally, and many
healthcare providers are under increasing pressure to reduce administrative and
operational expenses while maintaining the quality of care. AI-powered medical
imaging tools offer a solution to this challenge by streamlining workflows,
improving diagnostic accuracy, and reducing the time spent by radiologists on
each image.
By automating routine tasks such as image
segmentation, identification of abnormal regions, and classification of
findings, AI can significantly cut down the time radiologists spend analyzing
images. This automation not only enhances productivity but also reduces human
error, which is a major contributor to unnecessary costs in healthcare. For
example, the detection of abnormalities in medical imaging can help prevent
costly diagnostic errors and avoid unnecessary follow-up procedures. AI tools
are capable of handling large volumes of images, allowing healthcare facilities
to optimize their resources and scale operations without needing to hire
additional staff.
In addition, AI systems can help with the
management of healthcare workflows by reducing bottlenecks in the image review
process. Healthcare providers can prioritize critical cases, leading to more
efficient use of time and resources. The potential for operational cost
savings, particularly for hospitals and clinics under budget constraints, is a
major driving force for the adoption of AI technologies in medical imaging.
Shortage of Radiologists and Healthcare
Professionals
The shortage of radiologists and healthcare
professionals is a significant driver of AI adoption in medical imaging. The
increasing demand for medical imaging services, combined with a global shortage
of trained radiologists, has created a gap that AI technologies can help fill.
Radiologists are responsible for interpreting and analyzing imaging data, but
the growing demand for imaging services in many countries has placed a strain
on this workforce. Long working hours, high workloads, and a limited number of
radiologists contribute to delays in image interpretation, which can negatively
impact patient care. A 2020 survey published by Definitive Healthcare revealed that
approximately one-third of hospitals and imaging centers utilize AI, machine
learning (ML), or deep learning to support tasks related to patient care
imaging. Additionally, the growth of this segment is driven by the availability
of advanced medical imaging equipment in hospitals with robust infrastructure.
AI can help alleviate this shortage by automating
certain aspects of image analysis, enabling radiologists to focus on more
complex cases that require human judgment and expertise. AI-powered tools can
review routine images, highlight potential areas of concern, and even suggest
possible diagnoses, allowing radiologists to prioritize their workload more
effectively. This automation of repetitive tasks increases the efficiency of
the radiology department and allows the existing workforce to manage more cases
in less time, ultimately improving patient care without overburdening staff.
By integrating AI into the diagnostic workflow,
healthcare systems can bridge the gap between the increasing demand for medical
imaging and the limited number of radiologists available to interpret the data,
further driving the growth of AI in medical imaging.
Rising Focus on Personalized Healthcare and
Precision Medicine
Personalized healthcare and precision medicine are
gaining increasing attention globally, with a focus on delivering tailored
treatments based on individual patient characteristics. AI in medical imaging
plays a key role in this transformation by enabling the precise analysis of
imaging data to identify disease biomarkers and predict disease progression in
patients.
AI can help healthcare professionals identify
specific patient attributes and tailor treatment plans accordingly. For
example, AI-driven imaging solutions can analyze genetic information, medical
history, and imaging data to predict how a particular disease may progress in
an individual patient, thus allowing healthcare providers to customize
treatments that are more effective for each patient. This shift toward
personalized care and precision medicine is expected to accelerate the adoption
of AI in medical imaging, as AI tools become essential for providing more
accurate, patient-centric care. In May 2022, Atlantic Health System partnered
with Aidoc to implement an AI imaging solution aimed at helping physicians
accelerate care delivery and improve health outcomes.
As precision medicine continues to evolve, AI’s
ability to provide detailed insights from medical imaging data will be
instrumental in shaping the future of healthcare. This shift toward more
individualized treatment plans will be a major factor in the ongoing growth of
the AI in medical imaging market.
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Key Market Challenges
Integration with Existing Healthcare Systems
Another challenge is the integration of
AI-powered medical imaging solutions with existing healthcare infrastructure
and workflows. Healthcare organizations often use legacy systems for medical
imaging, electronic health records (EHR), and picture archiving and
communication systems (PACS). For AI to be effectively integrated into these
systems, there needs to be interoperability between AI tools and existing
technologies.
The integration of AI into clinical workflows can
be complex and resource-intensive, requiring healthcare organizations to invest
in new IT infrastructure and train staff to use the new technology. In many
cases, hospitals and clinics may also need to modify their existing data
management systems to accommodate AI tools and ensure that they can seamlessly
exchange data with other software applications. The transition to AI-powered
solutions can be time-consuming and costly, especially for smaller or resource-constrained
healthcare providers that may struggle to afford the necessary investments in
technology and training.
For AI systems to be fully effective in clinical
settings, they must be able to integrate not only with medical imaging software
but also with a wide range of other healthcare technologies, such as laboratory
information management systems (LIMS), EHRs, and patient management systems.
This integration challenge can create bottlenecks in the adoption of AI, as
healthcare providers often face difficulties coordinating these various
technological components.
Lack of Skilled Professionals and Training
A significant challenge to the adoption of AI in
medical imaging is the shortage of skilled professionals capable of
developing, deploying, and overseeing AI-powered solutions. The integration of
AI into medical imaging workflows requires not only radiologists but also AI
and machine learning experts, data scientists, and IT professionals. As AI
technologies evolve, there is a growing demand for specialists who understand
both the healthcare domain and the intricacies of AI and machine learning
algorithms.
Many radiologists, technicians, and clinicians may
not be fully trained in using AI tools effectively, which could impede the
smooth integration of AI systems into daily clinical practice. While AI can
significantly enhance diagnostic capabilities, its success depends on the
expertise of the professionals who use it. Radiologists must be trained to
interpret the results generated by AI systems and integrate these findings into
their overall clinical judgment. Lack of adequate training and understanding of
how AI models work can result in hesitation or reluctance among healthcare
professionals to trust and use AI-powered solutions.
The rapidly evolving nature of AI technologies
means that ongoing education and upskilling of healthcare professionals will be
necessary to ensure that they can keep up with advances in the field. This
continuous demand for training and skilled professionals presents a challenge
to the widespread adoption of AI-powered medical imaging solutions, especially
in developing regions with limited resources for education and training
programs.
Key Market Trends
Technological Advancements in AI Algorithms and
Machine Learning
Advancements in AI algorithms and machine learning
techniques are another key driver of the global AI in medical imaging market.
Over the past few years, machine learning, deep learning, and neural networks
have made significant strides, particularly in their ability to process complex
imaging data. These advancements have enabled AI algorithms to perform highly
accurate image recognition, classification, and interpretation tasks that were
previously unimaginable. In July 2022, the FDA granted Philips SmartSpeed AI-based software its
510(k) approval, allowing the company to provide groundbreaking high-speed,
high-resolution MR imaging. This software is highly compatible, enabling faster
and higher-quality scans for nearly all patients, including those with implants
(covering 97% of clinical protocols). Additionally, the advanced MR
acceleration software delivers scans up to three times faster, enhancing the
efficiency of MR departments while maintaining high-quality image resolution.
Deep learning models, such as convolutional neural
networks (CNNs), are now widely used in medical imaging because of their
ability to learn hierarchical features from large datasets. By training on vast
amounts of annotated imaging data, AI algorithms can identify subtle patterns
and features in medical images, such as tumors, fractures, or tissue
abnormalities, that are difficult for human clinicians to detect. These
breakthroughs in AI technologies have made them more reliable and applicable to
real-world healthcare settings, contributing significantly to the growing
adoption of AI in medical imaging.
Improvements in computational power and access to
large medical imaging datasets have enabled the development of more
sophisticated AI models. With the continued evolution of AI technologies and
algorithms, the medical imaging market is poised to experience further growth
and innovation.
Integration with Other Digital Health Technologies
The increasing integration of AI-powered medical
imaging solutions with other digital health technologies is driving the growth
of the market. Healthcare systems worldwide are becoming more digitized, with
the widespread use of electronic health records (EHRs), electronic
medical records (EMRs), and health information exchanges (HIEs). AI
technologies that can seamlessly integrate with these systems can provide
real-time support for healthcare professionals by automatically analyzing
medical images, extracting relevant data, and integrating this information into
the patient’s electronic medical record.
This integration allows for a more streamlined
workflow and ensures that healthcare providers have access to all relevant
data, including medical imaging, clinical notes, lab results, and treatment
histories, in one centralized location. The interoperability of AI-driven
imaging solutions with other digital health tools enhances the overall
efficiency and effectiveness of healthcare delivery, making them an attractive
option for healthcare providers and hospitals. As healthcare providers
increasingly adopt integrated digital health platforms, AI in medical imaging
will continue to experience significant growth.
Segmental Insights
End Use Insights
Based on the end use
segment, Hospitals dominated the adoption and deployment of AI
technologies compared to Diagnostic Imaging Centers. This dominance can
be attributed to several key factors, including the volume of patients, the
diversity of imaging needs, and the integration of AI into broader healthcare
workflows within hospital settings.
Hospitals, which typically offer a wide range of
medical services, including emergency care, surgery, oncology, cardiology, and
neurology, generate vast amounts of imaging data on a daily basis. The need for
accurate and efficient diagnostic tools is critical in these settings, where
timely decision-making can significantly impact patient outcomes. AI-powered
solutions, particularly deep learning algorithms and machine learning
models, have proven essential for enhancing diagnostic accuracy and
supporting radiologists in hospitals. These AI tools assist in detecting
abnormalities such as tumors, fractures, and other complex conditions across
various imaging modalities like X-rays, CT scans, MRIs, and ultrasounds. One of
the driving forces behind the widespread use of AI in hospitals is the
increasing complexity of medical imaging data. As hospitals manage large-scale
imaging operations with vast datasets, AI technologies have become invaluable
in automating routine tasks, such as image segmentation, pattern recognition,
and anomaly detection. By implementing AI tools, hospitals can improve
workflow efficiency, reduce diagnostic errors, and support radiologists in
making quicker, more accurate decisions. This is especially important given the
global shortage of radiologists, with AI filling the gap by allowing healthcare
professionals to focus on more complex cases.
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Regional Insights
North America dominated the Global AI in Medical Imaging Market,
largely due to its advanced healthcare infrastructure, significant
technological advancements, and strong market demand for innovative diagnostic solutions.
The region's leadership in the AI-powered medical imaging space is driven by a
combination of factors, including a well-established healthcare system, high
investment in research and development (R&D), a large number of healthcare
providers and technology companies, and a supportive regulatory environment.
One of the key factors driving the dominance of
North America is its highly developed healthcare infrastructure. The
U.S., in particular, has some of the most advanced hospitals, clinics, and
diagnostic imaging centers in the world, which are equipped with
state-of-the-art imaging devices such as MRI machines, CT scanners, and X-ray
systems. The sheer volume of medical imaging data generated in these healthcare
settings creates a strong demand for AI technologies to automate processes,
reduce diagnostic errors, and enhance the efficiency of medical imaging
workflows. North America is home to many of the world’s leading healthcare
technology companies and AI startups that specialize in medical
imaging solutions. These companies are at the forefront of developing AI-driven
tools that assist in tasks such as image recognition, pattern analysis, and
anomaly detection, making the region an attractive hub for AI innovation.
The U.S. government has also played a significant
role in driving the adoption of AI in medical imaging through initiatives aimed
at fostering the growth of digital health technologies. Regulatory bodies such
as the Food and Drug Administration (FDA) have provided clear guidelines
for AI tools and have approved several AI-based imaging devices for clinical
use. This has led to greater confidence in AI technologies among healthcare
providers and has accelerated their integration into routine clinical practice.
The regulatory environment in North America is generally favorable toward the
development and deployment of AI solutions, providing a level of certainty for
AI companies seeking to bring their products to market.
Recent Developments
- In January 2024, GE HealthCare announced its
acquisition of MIM Software, a Cleveland-based global leader in medical imaging
analysis and AI solutions across molecular radiotherapy, radiation oncology,
urology, and diagnostic imaging. This acquisition aims to integrate MIM
Software's imaging analytics and digital workflow capabilities into various
care areas, driving innovation and enhancing GE HealthCare's offerings to
positively impact both patients and healthcare systems worldwide.
- In November 2023, GE HealthCare introduced its
MyBreastAI suite at the RSNA 2023 conference. This cutting-edge product is
designed to streamline radiologists' workflows by providing advanced tools that
assist in the early detection and diagnosis of breast cancer, ultimately
improving patient outcomes.
- In November 2023, Canon Medical Systems launched
two of four new computed tomography (CT) scanners featuring the upgraded
Aquilion CT platform. These scanners incorporate artificial intelligence
algorithms to enhance image quality and simplify scanner workflows, offering
improved performance for healthcare providers.
- In September 2023, COTA, a company specializing in
real-world oncology data and analytics, unveiled Vista, an expansive automated
EHR dataset aimed at accelerating cancer research and applying generative AI in
cancer care. Vista uses automated data abstraction, machine learning
algorithms, and medical expert oversight to extract clinically relevant
information from electronic medical records, providing biopharmaceutical
companies with timely insights to speed up the development of life-saving
therapies.
Key Market Players
- Digital Diagnostics Inc.
- Tempus AI, Inc.
- Advanced Micro Devices, Inc.
- HeartFlow, Inc.
- Enlitic, Inc.
- Viz.ai, Inc.
- EchoNous Inc.
- HeartVista Inc.
- Exo Imaging, Inc.
- Nano-X Imaging Ltd.
|
By Technology
|
By Application
|
By Modalities
|
By End Use
|
By Region
|
- Deep
Learning
- Natural
Language Processing
- Others
|
- Neurology
- Respiratory
& Pulmonary
- Cardiology
- Breast
Screening
- Orthopedics
- Others
|
- CT scan
- MRI
- X-rays
- Ultrasound
- Nuclear
Imaging
|
- Hospitals
- Diagnostic
Imaging Centers
- Others
|
- North
America
- Europe
- Asia Pacific
- South
America
- Middle East
& Africa
|
Report Scope:
In this report, the Global AI In Medical Imaging Market
has been segmented into the following categories, in addition to the industry
trends which have also been detailed below:
- AI In Medical Imaging Market, By Technology:
o Deep Learning
o Natural Language Processing
o Others
- AI In Medical Imaging Market, By Application:
o Neurology
o Respiratory & Pulmonary
o Cardiology
o Breast Screening
o Orthopedics
o Others
- AI In Medical Imaging Market, By Modalities:
o CT scan
o MRI
o X-rays
o Ultrasound
o Nuclear Imaging
- AI In Medical Imaging Market, By End Use:
o Hospitals
o Diagnostic Imaging Centers
o Others
- AI In Medical Imaging Market, By Region:
o North America
§ United States
§ Canada
§ Mexico
o Europe
§ France
§ United Kingdom
§ Italy
§ Germany
§ Spain
o Asia-Pacific
§ China
§ India
§ Japan
§ Australia
§ South Korea
o South America
§ Brazil
§ Argentina
§ Colombia
o Middle East & Africa
§ South Africa
§ Saudi Arabia
§ UAE
Competitive Landscape
Company Profiles: Detailed analysis of the major companies present in the Global AI
In Medical Imaging Market.
Available Customizations:
Global AI In Medical Imaging market report with
the given market data, TechSci Research offers customizations according to a
company's specific needs. The following customization options are available for
the report:
Company Information
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market players (up to five).
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