Dublin, Nov. 26, 2024 (GLOBE NEWSWIRE) -- The "Artificial Intelligence (AI) in Medical Imaging Market - Focused Insights 2024-2029" report has been added to ResearchAndMarkets.com's offering.
The Artificial Intelligence (AI) in Medical Imaging Market was valued at USD 2.81 billion in 2023, and is expected to reach USD 29.28 billion by 2029, rising at a CAGR of 47.79%
The global AI in medical imaging market report contains exclusive data on 46 vendors. The AI in medical imaging market is highly fragmented, with limited global presence, and several regional and local players are offering AI in medical imaging solutions in a wide range.
The major global AI players in medical imaging are Siemens Healthineers, General Electric, Koninklijke Philips, IBM Watson Health, and Fujifilm. Global players focus on developing innovative products with advanced technologies and expanding their product portfolio to remain competitive. They continuously invest extensively in R&D and product development activities to expand their portfolio.
The U.S. dominates the global AI in medical imaging market with the largest share in the North American region. The AI in the medical imaging market in North America is experiencing significant growth, driven by advancements in technology, the increasing prevalence of chronic diseases, and the need for efficient and accurate diagnostic tools.
The U.S. dominates the region because many radiologists and hospitals prefer AI in medical imaging software/applications. The increased adoption is also observed due to a good healthcare system in the U.S. With ongoing advancements in AI technologies and increasing investments in research and development, the market is expected to see widespread adoption across healthcare settings. The focus will be improving diagnostic accuracy, enhancing patient outcomes, and streamlining workflows to meet the growing demand for efficient and effective medical imaging solutions.
MARKET TRENDS & DRIVERS
Advancements in Deep Learning and Neural Networks
Technological advancements in AI for medical imaging are revolutionizing the field by enhancing diagnostic accuracy, increasing efficiency, and reducing costs. Deep learning and neural networks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are widely used for image recognition and sequence analysis, respectively, making them ideal for analyzing various medical images. Automated image analysis, including segmentation, detection, and pattern recognition, helps radiologists focus on specific areas of interest, such as delineating tumor boundaries or identifying disease-specific patterns.
Natural Language Processing (NLP) further aids in generating detailed reports and providing clinical decision support by extracting relevant information from patient records. Seamless integration with Picture Archiving and Communication Systems (PACS), Radiology Information Systems (RIS), and cloud-based solutions streamlines workflows and facilitates collaborative research. Enhanced imaging techniques like 3D, 4D, and multi-modal imaging, combined with real-time image enhancement and reconstruction, improve the quality and comprehensiveness of medical imaging.
Predictive analytics and personalized medicine benefit from AI's ability to assess risk, predict outcomes, and tailor treatment plans based on specific disease characteristics. Increasing numbers of AI in imaging tools are receiving regulatory approvals, ensuring their safety and efficacy, while standardization efforts aim to maintain consistency across healthcare settings. Collaborative research and open data initiatives, supported by large annotated medical imaging datasets, are accelerating the development and implementation of AI in Medical Imaging.
Virtual and Augmented Reality with 3D Medical Imaging
Virtual and augmented reality (VR/AR) technologies are revolutionizing the healthcare industry, particularly in medical imaging. By integrating VR/AR with 3D medical imaging data, healthcare professionals gain unprecedented visualization and planning capabilities for various medical procedures. Augmented reality offers unique benefits in medical settings. AR technology overlays digital information onto the user's real-world environment, enabling clinicians to visualize medical data in the context of the patient's anatomy.
Furthermore, AR can facilitate high-risk operations by providing surgeons with enhanced visualization and decision-support tools. For instance, AR overlays vital patient information, such as medical images, diagnostic data, and real-time physiological parameters, directly into the surgeon's field of view during surgery. This augmented information enables surgeons to make informed decisions and perform complex procedures more confidently and accurately.
Overall, integrating VR/AR technologies with 3D medical imaging data offers transformative benefits in healthcare, ranging from enhanced visualization and planning capabilities to improved surgical precision and patient outcomes. As these technologies continue to advance, they have the potential to revolutionize medical practice and redefine standards of care across various medical specialties.
Addressing the Surge in Medical Imaging Demand through AI Solutions
The escalating incidence of chronic diseases, such as cancer, cardiovascular ailments, and neurological disorders, has precipitated a surge in the demand for medical imaging procedures. This upsurge places considerable strain on healthcare systems, underscoring the urgent need for innovative solutions to manage the growing workload. AI technology has emerged as a transformative tool in medical imaging. By harnessing the power of machine learning algorithms and deep learning techniques, AI systems can automate various facets of the imaging process, from image analysis to interpretation. This automation enhances the efficiency and speed of diagnosis and improves the accuracy and reliability of results.
Moreover, AI-enabled medical imaging facilitates early detection and characterization of diseases, enabling timely interventions and personalized treatment strategies. This convergence of rising chronic disease prevalence and the adoption of AI in Medical Imaging represents a paradigm shift in healthcare delivery, promising improved patient outcomes, enhanced workflow efficiency, and greater diagnostic precision. As AI continues to evolve and integrate into clinical practice, its role in medical imaging is poised to expand, offering new opportunities to address the challenges posed by the growing burden of chronic diseases.
Demand for almost all aspects of diagnostic procedures is increasing every year. The widespread consensus is that demand will continue to rise due to the increasing prevalence of chronic illnesses worldwide. The increase in the demand has been driven partly by an increase in imaging activity across many aspects of acute hospital activity, with increases in demand from urgent referrals for cancer (10% per year) imaging. Wider indications for tests such as CT scanning are also fuelling the demand.
INDUSTRY RESTRAINTS
Lower Adoption in LMICs
Low- and middle-income countries (LMICs) face significant barriers to adopting artificial intelligence (AI) in healthcare. Limited financial resources, inadequate digital infrastructure, and a shortage of skilled professionals hinder the implementation of AI in solutions. Moreover, cultural skepticism, divergent public policies, and unique medical practice patterns pose additional challenges. These factors contribute to lower adoption rates of AI technologies in LMICs compared to high-income countries. Addressing these barriers requires targeted investments in digital health infrastructure, capacity-building initiatives, and collaborative efforts between governments, healthcare providers, and technology developers.
By overcoming these challenges, LMICs can harness the potential of AI to improve healthcare delivery, enhance diagnostic accuracy, and, ultimately, advance public health outcomes. Implementing AI in a country where there is a lack of education can result in more challenges. Successful AI adoption in low- and middle-income countries needs education of local radiology leadership in AI validation. A common problem in AI deployment is algorithm generalizability.
Key Attributes:
| Report Attribute | Details |
| No. of Pages | 165 |
| Forecast Period | 2023 - 2029 |
| Estimated Market Value (USD) in 2023 | $2.81 Billion |
| Forecasted Market Value (USD) by 2029 | $29.28 Billion |
| Compound Annual Growth Rate | 47.7% |
| Regions Covered | Global |
Key Vendors
- General Electric
- Siemens Healthineers
- Koninklijke Philips
- IBM Watson Health
- Fujifilm
Other Prominent Vendors
- Agfa-Gevaert
- Avicenna.AI
- AZmed
- Butterfly Network
- CellmatiQ
- dentalXrai
- Digital Diagnostics
- EchoNous
- Gleamer
- iCAD
- Lunit
- Mediaire
- Microsoft
- NVIDIA
- Intel
- Nanox Imaging
- Paige AI
- Perimeter Medical Imaging AI
- Autodesk
- Aidence
- ContextVision
- Exo
- Predible Health
- Paragon Biosciences
- 1QB Information Technologies
- Qure.ai
- Quantib
- Quibim
- Vista.ai
- Renalytix
- Tempus
- Therapixel
- Ultromics
- Viz.ai
- VUNO
- Merative
- Google Cloud
- InformAI
- HeartFlow
- Enlitic
- Brainomix
For more information about this report visit https://www.researchandmarkets.com/r/8lym7a
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