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February 2025
The AI in medical imaging market size is forecasted to expand from USD 1.79 billion in 2025 to USD 26.23 billion by 2034, growing at a CAGR of 34.8% from 2025 to 2034, as a result of the rising number of cross-industry collaborations, and extensive adoption of big data.
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Artificial Intelligence (AI) has been rapidly emerging as a revolutionary technology in various industries. In healthcare, AI has the potential to revolutionize medical diagnosis and treatment, particularly in the field of diagnostic medical imaging. Diagnostic imaging, including X-rays, MRI scans, and CT scans, generates vast amounts of data that can be difficult for healthcare professionals to analyze accurately and efficiently. The integration of AI in diagnostic imaging can help overcome this challenge and improve the accuracy and efficiency of medical diagnosis.
AI exclusively analyses complex data using computerized algorithms. Diagnostic imaging is one of the most potential clinical applications of AI, and increasing focus is being paid to establishing and optimizing its performance to make it easier to detect and quantify a variety of clinical disorders. Studies utilizing computer-aided diagnostics have demonstrated great specificity, sensitivity, and accuracy for the detection of minor radiographic abnormalities, with the potential to enhance public health. A lot of research is being done on the application of artificial intelligence in diagnostic medical imaging. AI has demonstrated outstanding sensitivity and accuracy in identifying imaging abnormalities, and it has the potential to improve tissue-based diagnosis and characterization.
AI can help radiation therapy and medical imaging employees work more efficiently by automating repetitive operations. Shorter examination periods and more time for patient care and engagement could be the results of this automation, which would increase patient satisfaction and speed up the time it takes to diagnose an emergency. There are no disputing AI's current and future effects on patient care. By evaluating data that promotes improved public and individual health, AI technologies are proven beneficial in-patient care. One of AI's advantages in medicine is its capacity to handle thousands more data points more quickly than a human ever could. People somewhat rely on learned experience while making these important decisions.
Consumer health tracking data can be used by internet-based health indicators to conduct public health research that goes above and beyond what is often possible. Social indicators can close any remaining gaps in patient health and well-being improvement with data not available from medical sources. AI may collect population data to combine knowledge about communities and populations for better epidemiology, better chronic illness prevention, and better management. Early results on separating benign from malignant nodules on chest CT scans, coupled with neurologic and psychiatric uses, suggest that applying AI to advanced imaging modalities such as CT and MR is already showing significant promise. The use of AI in MR to predict survival in patients with cervical cancer and Amyotrophic Lateral Sclerosis has shown potential, subsequently impacting the market growth.
Big data (huge and complex data) is produced at various phases of the care delivery process as a result of the industry's growing digitization and adoption of information systems. Big data in the field of medical imaging includes information derived from clickstream and web & social media interactions, readings from medical devices like sensors, ECGs, X-rays, and other billing records, as well as biometric data and information from other sources. With the increasing acceptance of EHRs, digitized 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.
In the future years, the volume of big data in medical imaging is expected to rise due to the usage of bidirectional patient portals, which allow patients to upload their digitized medical records, data, and images (EMRs). The healthcare sector is turning its attention to a variety of artificial intelligence (Al)-based solutions in order to effectively manage the volume of huge and complex medical diagnostic data that is constantly growing. For instance, a senior radiologist would need at least 20 minutes to examine a tomography (CT) scan that has 200–400 images, whereas an AI-based CT scan reading just needs 20 seconds. In 2020, Kang Zhang's team, led by a professor at MUST's Faculty of Medicine, created an AI imaging-assisted detection method for COVID-19 pneumonia and published their findings in Cell. The method was able to distinguish COVID-19 from other viral pneumonia within 20 seconds with an accuracy rate of more than 90%, based on the 500,000 copies of CT images that the team evaluated.
One of the most promising areas of health and medical innovation is the use of AI in medical imaging. Applications for object detection and picture categorization are getting more reliable and accurate with the advancement of machine learning algorithms. Machine learning-based techniques are therefore being used in the healthcare industry. Deep learning techniques are being used, in particular, in a variety of medical applications, including the automatic segmentation of the left ventricle in cardiac MRI and the diagnosis of diabetic retinopathy in retinal fundus images. It is therefore worthwhile to investigate deep learning-based techniques that can be used to locate and count the blood cells in smear images. Medical imaging also uses AI in a variety of ways, including image acquisition, processing for assisted reporting, follow-up planning, data storage, data mining, and more.
AI has demonstrated impressive sensitivity and precision in the classification of imaging abnormalities in recent years, and it assures to enhance tissue-based detection and characterization. Machine Learning (ML) uses computational models and algorithms that mimic the structure of the brain's biological neural networks. Layers of interconnected nodes make up the architecture of neural networks. The input values are weighted summarized at each node in the network and then passed on to an activation function. The adoption 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. Additionally, AI plays a crucial role in the processing of a large volume of medical photos, which reveals disease symptoms that are otherwise missed. As a result, it is anticipated that the market for AI in medical imaging will expand during the coming years.
The use of AI in healthcare and medical imaging has been increasing due to the benefits provided by AI techniques, such as improved accuracy, efficiency, and reduced workload. To develop innovative AI-based solutions for healthcare applications, various companies in the AI medical imaging market have started collaborating with top AI technology providers. By doing so, they can offer advanced solutions to their customers and strengthen their position in the highly competitive market. This trend is expected to continue as the demand for AI-based medical imaging solutions grows. Achieving improvements are motivating businesses to gain momentum in terms of generating cutting-edge products and tools due to the growing demand for AI-based solutions. However, there are a number of sectors where the impact of AI technology is anticipated to be significant. Enhanced image diagnosis, predictive image analysis, workflow efficiency with data analysis, report production, and prioritizing of important discoveries are some examples of this.
The market players in the field of AI in medical imaging are likely to benefit from this by creating new opportunities. A cooperation between AccentCare and Swift Medical to supply technologies to thousands of healthcare practitioners was announced in June 2020 by the American healthcare corporation. This will help the authorities decide on treatments quickly. A multi-year strategic collaboration to conduct research with Mayo Clinic was signed, according to Visage Imaging, a division of Pro Medicus Limited, an Australian business, in June 2020.
Healthcare professionals can now help patients through cutting-edge treatment modalities due to the extensive rise of digital health. With the use of modern technology, doctors can better diagnose and treat their patients. However, it has been noted that doctors are reluctant to adopt new technologies. For instance, doctors mistakenly believe that Al will take the place of doctors in the near future. Radiologists and doctors concur that human abilities like empathy and persuasion exist. Therefore, the use of technology cannot entirely replace the need for a doctor. Furthermore, it is feared that patients may have an unhealthy preference for these technologies and avoid crucial in-person therapies, which could strain long-term doctor-patient relationships.
To persuade providers that the Al-based solutions are affordable, efficient, and secure alternatives that allow the doctors with convenience and better patient care, the argument is evolving. However, healthcare organizations are beginning to recognize the range of uses and potential advantages of Al-based solutions. As a result, it is possible that in the years to come, medical professionals like radiologists will be more likely to use Al-based technology. Since radiologists and practitioners view the AI technology cannot completely replacing the need for a specialist. However, many medical professionals are also currently skeptical about the capabilities of AI systems to accurately diagnose patient diseases. Therefore, it is difficult to convince the providers that AI-based solutions are affordable, useful, and secure solutions that support physicians and improve patient care. Thus, it can hamper the growth of the market.
The actual goals during the development of Al technologies were to make them human-aware or to create models that resembled how people actually think. A system that combines human intelligence and an artificial intelligence (AI) approach is known as human-aware technology or human-aware augmented intelligence. The concept is that it can perform better than either a human or a piece of technology can by itself. Human-aware technology incorporates elements of intelligence that support human collaboration, such as emotional and social intelligence, and works in harmony with humans. As human-aware technology progressively permeates our daily lives, there are no limits to what it can do. Human-aware technology can improve human capacities and pave the road for human achievement, from intelligent prostheses to intelligent coaching. However, building interactive and scalable AI machines that model human mental states in the loop, recognize their desires and intentions, offer proactive support, exhibit explicable behavior, provide cogent explanations upon request, and inspire trust continue to be challenges for AI machine developers. Additionally, growth in human involvement with AI approaches and interest in learning about machine learning has created new research obstacles, such as those related to interpretation and presentation, issues with automated components, and intelligent crowdsourced control. Al machines have trouble understanding particular instructions and information when it comes to interpretation. Presentation challenges include issues related to delivering the AI system's output and feedback mechanisms Thus, developing the human-aware AI systems remains the foremost opportunity for the AI developers.
AI has the potential to revolutionize medical imaging, and hospitals and clinics are already beginning to adopt the technology. The use of AI in medical imaging has several potential benefits, including increased accuracy, improved efficiency, and reduced costs. One of the primary ways that AI is being used in medical imaging is through computer-aided diagnosis (CAD) systems. These systems use machine learning algorithms to analyze medical images and identify potential areas of concern. For example, in mammography AI can be used to identify potential breast cancer lesions, helping radiologists make more accurate diagnoses. Similarly, in lung imaging, AI can be used to identify potential nodules or tumors.
As there are numerous scenarios in which a patient can require medical imaging, hospitals and healthcare organizations are implementing AI-powered imaging systems. Whether it is a heart event, fracture, neurological disorder, or chest problem, AI can instantly assess the condition and offer therapeutic choices. The majority, if not all, of the resources used by nearly one-third of all AI SaaS businesses in the healthcare sector, are allocated to diagnostics. In diagnostic medical imaging, artificial intelligence (AI) is being carefully examined. AI has demonstrated outstanding sensitivity and accuracy in detecting picture anomalies, and it has the potential to enhance tissue-based detection and characterization. AI in hospitals is now able to assist clinicians as a thorough diagnosis tool that will alter the results of many cases. Many different types of cancer are notoriously difficult to diagnose and treat. As a result, to carefully check the tissue for cancer cells, hospitals, and other healthcare facilities are implementing machine-learning software and AI imaging techniques. A hospital must also have a patient flow that is optimized in order to diagnose as many patients as feasible quickly. Each patient receives quick care owing to the use of AI in imaging.
Computed tomography (CT) imaging is a widely used diagnostic tool that provides detailed images of the inside of the body. It involves the use of X-rays and computer algorithms to create 3D images of the body, which can be used to diagnose a wide range of medical conditions.
In recent years, the use of artificial intelligence (AI) in CT imaging has become increasingly common, with the potential to improve accuracy and efficiency in diagnosis. AI algorithms can be trained to identify subtle changes in the CT images that might be missed by a human radiologist. This can lead to earlier detection of disease, improved accuracy in diagnosis, and better treatment outcomes for patients.
However, there are also potential pitfalls associated with the use of AI in CT imaging. One concern is that relying too heavily on AI algorithms could lead to a decrease in the quality of care, as radiologists may become less skilled at interpreting images without the help of AI. Additionally, there is a risk of overdiagnosis and overtreatment if AI algorithms are too sensitive, leading to unnecessary medical procedures and increased healthcare costs.
To balance the promise and pitfalls of AI in CT imaging, it is important to consider several factors. First, radiologists must be properly trained in the use of AI algorithms to ensure that they are used effectively and appropriately. Second, AI algorithms must be validated in large-scale studies to ensure their accuracy and reliability. Third, there must be clear guidelines in place for how AI should be used in CT imaging, to prevent overdiagnosis and overtreatment.
Thus, the use of AI in CT imaging holds great promise for improving patient outcomes and advancing the field of medical imaging. However, it is important to approach this technology with caution and to carefully balance its potential benefits with the potential risks and pitfalls. By doing so, we can ensure that AI in CT imaging is used to its fullest potential, while still providing high-quality care to patients.
By technology, the deep learning segment registered its dominance over the global AI in medical imaging market in 2024. Deep learning is an advanced technology of machine learning that helps radiologists provide more accurate diagnoses. This technology is widely used for medical image classification. This provides radiologists with a deeper understanding of the disease, reducing manual errors. Thus, deep learning involves classification, segmentation, detection, tracking, and retrieval applications of non-invasive imaging methods. These superior benefits of deep learning in medical imaging potentiate the segment’s growth.
By technology, the natural language processing segment is expected to grow at the fastest rate in the market during the forecast period. NLP is focused on the computer interpretation of human language and is used to evaluate radiology reports. It is predominantly used in diverse applications such as the interpretation of imaging data, improving image acquisition, image analysis, and increasing the efficiency of imaging services. This reduces the workload on radiologists and healthcare professionals, providing accurate diagnoses. The increasing demand for AI and ML in medical imaging augments the segment’s growth.
By application, the neurology segment led the global AI in medical imaging market in 2024. The increasing incidences of neurological disorders and technological advancements propel the segment’s growth. According to the World Health Organization, more than 3 billion people globally are estimated to be living with neurological conditions. The most common imaging technique for identifying neurological conditions is the MRI. The complexity of the brain and neurological disorders necessitate the use of AI in medical imaging. AI in MRI can identify minute anomalies in the brain, enhancing the potency of disease diagnosis.
By application, the breast screening segment is anticipated to grow with the highest CAGR in the market during the studied years. The increasing incidences of breast cancer and its complexity fuel the segment’s growth. 2D mammography and digital breast tomosynthesis are the imaging techniques used for screening and diagnosing breast cancer. AI can help to detect hidden tumor cells or tissues in a dense breast, enhancing accuracy and precision. AI in mammography screening can significantly reduce screen-reading workload, helping healthcare professionals understand the clinical impact of the disease.
North America is currently the leading region in the global AI in medical imaging market due to the high adoption of advanced healthcare technologies, increasing prevalence of chronic diseases, and significant investments in research and development. The United States is the largest market in this region, with several leading players such as GE Healthcare, IBM Watson Health, and Philips Healthcare. Favorable regulatory policies support the development of AI-enabled medical imaging techniques in North America. The Food and Drug Administration (FDA) regulates innovative, safe, and effective medical devices incorporating AI/ML in the U.S. As of August 2024, the U.S. FDA approved 950 AI/ML-enabled devices.
The Asia-Pacific region is expected to witness significant growth in the AI in medical imaging market due to the increasing demand for advanced medical imaging technologies, the growing geriatric population, and rising healthcare expenditure in countries such as China, India, and Japan. The major players in this region include Hitachi, Ltd., Fujifilm Holdings Corporation, and Koninklijke Philips N.V. Favorable government policies and increasing investments augment the market. As of June 2024, China’s NMPA approved a total of 92 AI tools to accelerate healthcare decision-making and improve diagnostic accuracy. The Indian government also actively supports the integration of AI in every sector, including healthcare. AI expenditure in India is expected to reach $11.78 billion by 2025 and is also estimated to add $1 trillion to India’s economy by 2035.
Prashant Warier, co-founder and CEO of Qure.ai, commented that AI in medical imaging is still in its infancy and that the company is starting to integrate multiple data points to create more holistic AI models. This will enable doctors to make more informed decisions by considering a patient’s full medical history along with diagnostic imaging.
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