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AI in radiology:
top use cases, examples & best practices
August 6, 2026
AI in radiology helps analyze medical imaging data like X-rays, MRIs, and CT scans to identify subtle abnormalities and predict health trajectories, enabling timely diagnostics and personalized care planning, as well as facilitates radiology workflow automation, improving clinician efficiency.
Owing to recent advancements in artificial intelligence and computational capabilities, AI-powered radiology tools are showing great promise in the field of diagnostic imaging. Able to quickly process vast amounts of image data and detect subtle patterns and abnormalities in it, AI solutions promise to enhance diagnostic accuracy, streamline clinical workflows, improve clinical decision-making, and ultimately lead to better patient population outcomes.
AI in radiology market statistics
| The AI in radiology market is expected to reach $2.32 billion in 2026 and grow to $7.19 billion by 2031. | |
|---|---|
| The image analysis segment holds the largest AI in radiology market share of 39.3%. | |
| About 50% of healthcare and life science providers use artificial intelligence for medical imaging and diagnostics. |
Scheme title: Top three AI in healthcare use cases
Data source: NVIDIA
Top AI use cases in radiology
The number of use cases of artificial intelligence software in clinical data science and radiology practice is growing. Currently, AI in radiology is used to enhance cardiac imaging, classify brain tumors, detect vertebral fractures and neurological problems, optimize dose levels, and streamline radiology reporting.
Enhancing cardiac imaging
AI radiology solutions improve cardiac imaging by providing real-time, high-quality visualization of heart structures. For example, AI systems can automatically colorize heart chambers on grayscale echocardiograms, which reduces manual effort and streamlines radiologist workflows.
Philips developed an AI-enabled computed tomography scanner for cardiac imaging that supports image reconstruction, coronary visualization enhancement, and intelligent patient positioning, providing high-quality images at ultra-low dose levels. The solution also offers real-time remote collaboration capabilities to ensure alignment between different medical specialists and instant troubleshooting, as well as staff training tools for seamless solution adoption.
Classifying brain tumors
In order to identify the type of tumor, doctors take a biopsy, MRI scans, and blood tests. Once the type of tumor is determined, they can use radiomics AI tools to further stratify the tumor into one of several grades. AI accurately classifies brain tumors into grades with very few false positives or negatives, while significantly accelerating the classification process compared to conventional methods. This means that doctors can rely on AI as an additional tool to support their treatment decisions.
Spotting vertebral fractures
Spinal fractures, being the most common fragility fractures, can be an early sign of osteoporosis. However, CT scans frequently fail to identify vertebral compression fractures (VCFs), with more than half of them going unreported. To reduce rates of undiagnosed osteoporosis, AI algorithms can be used to analyze CT scans and X-ray images and detect VCFs. IB Lab FLAMINGO, AI-based radiological image processing software, can detect and classify clinically relevant vertebral compression fractures within CT scans, enhancing patient diagnosis and helping clinicians make more informed treatment decisions.
100%
Image title: IB Lab FLAMINGO assessment result
Data source: ImageBiopsy Lab
Detecting Alzheimer’s disease
Another challenging neurological disease to diagnose is Alzheimer’s. Researchers at the Mayo Clinic have developed an AI-powered solution called StateViewer that analyzes FDG-PET scans, compares them to scans from people with confirmed dementia diagnoses, and identifies patterns that match specific types of dementia, including Alzheimer's disease. As research has shown, the solution can identify the dementia type in 88% of cases, speeding up brain scan processing by nearly two times and delivering up to three times greater accuracy than standard workflows.
Other than that, AI algorithms can detect subtle brain changes and glucose uptake shifts well before symptoms appear. Early detection through AI allows doctors to start treatments that can significantly delay or even stop disease progression.
Diagnosing ALS
A degenerative neurological disease can be devastating, but its early detection allows doctors to draw up an effective long-term patient care plan. Amyotrophic lateral sclerosis (ALS) is a fatal degenerative disease that differs radically from PLS (a non-fatal variation). Diagnostics of these diseases rely on image analysis, where radiologists decide whether the existing lesions are actual ALS lesions or just mimicking them, thus pointing them to PLS. False positives are very common in this area. Using sophisticated machine learning models, it becomes possible to identify risk ratios for evidence of ALS or PLS. Mayo Clinic researchers have developed an AI model that can predict amyotrophic lateral sclerosis and differentiate between ALS and conditions with similar symptoms.
Assisting with radiology reporting & data-related tasks
Reporting is a time-consuming and error-prone task and is therefore often a source of frustration for radiologists. Furthermore, there are no set-in-stone reporting standards, which leads to variability and a lack of compatibility between data submitted by radiologists.
Natural language processing tools and generative AI software offer valuable capabilities for streamlining radiology reporting, from a quick transcription of speech into text to automated report compilation and their logical structuring for improved comprehension.
In addition to radiology reporting itself, AI-based solutions can perform related tasks, such as enhancing the quality of scans.
Detecting breast cancer
It is estimated that in 2026, 321,910 American women will be diagnosed with invasive breast cancer and 60,730 with ductal carcinoma in situ, making breast cancer the most commonly diagnosed type of cancer among women. To ensure the disease is detected early and increase chances of survival, clinics worldwide increasingly employ AI-powered tools. For example, Google offers an AI system for mammography that can spot signs of cancer invisible to humans more accurately, quickly, and consistently.
Dose optimization
Since medical imaging entails exposure to ionizing radiation, which carries health risks, including potential carcinogenic effects, optimizing radiation dose is essential to mitigate potential patient harm. AI helps optimize radiation doses with capabilities such as image reconstruction, camera auto-positioning, and image denoising, enabling accurate disease detection and preventing extra radiation associated with redundant scans. Research indicates that AI helps reduce radiation dose by approximately 40-90% across various types of CT scans, demonstrating the highest value in chest screening, oncological monitoring, vascular imaging, and pediatrics.
Detecting pneumonia
Radiology is a common method for detecting pneumonia, a serious lung disease. The problem with pneumonia detection using clinical imaging is that it is often hard for medical professionals to distinguish it from other lung diseases like bronchitis. AI systems, on the other hand, can detect and segment areas of opacity or consolidation indicative of pneumonia, identifying it with greater accuracy.
Based on a meta-analysis of AI algorithms, AI systems can markedly enhance pneumonia detection, catching about 90% of larger occult lung nodules.
Detecting LVO
Large vessel occlusion (LVO) strokes happen when a major artery in the brain is blocked, making it one of the most severe kinds of strokes that is often associated with an increased risk of death or long-term disability.
AI solutions for image segmentation can process MRA and CT images to identify and isolate the blood vessels in medical images for precise localization and characterization of potential occlusions. AI algorithms can then analyze the morphology, size, and integrity of the blood vessels, helping radiologists to reliably diagnose and triage LVO strokes.
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Examples of AI radiology solutions
Hospitals across the globe integrate AI into daily radiology workflows. Real-life examples of the leading AI-powered solutions that can support specialists in the reading room with routine image interpretation and patient diagnostics include Viz LVO, Lunit INSIGHT MMG, Rapid ICH, and qXR.
Viz, a medical imaging company that is dedicated to optimizing emergency treatment using deep learning algorithms, developed Viz LVO, a clinically validated AI-based technology for the automation of LVO stroke detection and triage. In 2018, Viz LVO was approved by the FDA, creating a new category of medical devices: computer-assisted triage. The device can automatically analyze computer tomography angiography (CTA) of the brain and identify LVO. A peer-reviewed study found that Viz LVO has a high sensitivity of 96.3% and specificity of 93.8%, making it a reliable companion for neuroradiologists.
Image title: Viz LVO notification & mobile interface
Image source: openaccessjournals.com — AI-powered stroke triage system performance in the wild
Viz alerts my team to all potential LVOs in our network and allows me to quickly view them on my phone. This is the new standard for stroke care.
Lunit INSIGHT MMG is AI-based software that helps radiologists more accurately assess mammograms. The deep learning solution can detect suspicious lesions in mammography images and distinguish tumor areas by providing the location of the lesion. Lunit software detects breast cancer on mammograms with 96% accuracy. The solution can help radiologists detect more breast cancer cases faster, improve the speed of triage, and enhance reading performance.
Image title: Lunit in action
Image source: lunit.io — Product Brochure Lunit INSIGHT MMG
Lunit's AI tool is particularly effective in helping diagnose dense breasts, which are often more likely to be misdiagnosed among East Asian women, and has helped improve communication between radiologists and breast surgeons.
An intracranial hemorrhage (ICH) refers to bleeding between the brain tissue and skull that can cause severe brain damage and death. Rapid ICH, developed by RapidAI, a global leader in AI-led solutions for vascular and neurovascular conditions, allows medical professionals to make fast, evidence-based decisions in critical situations and accurately detect bleeding on non-contrast CT images.
Rapid ICH's AI-based solution can identify hemorrhages with a sensitivity of 95% and specificity of 94% in as few as 3 minutes. Given that any hemorrhage requires a medical emergency, Rapid ICH automatically sends notifications via a dedicated mobile app, alerting clinicians about critical cases.
Image title: Rapid ICH + Hyperdensity solution interface
Image source: rapidai.com — Hemorrhagic Stroke Solutions
The decision we ultimately made was based largely on data from the RapidAI perfusion imaging. To have perfusion data say that this patient has a 200CC area of potential at risk, it helps give you that confidence that it's important to intervene.
qXR, developed by Indian AI startup Qure, is a tool that helps radiologists detect chest pathologies indicative of lung cancer in radiography imaging. With the help of a proprietary deep learning model trained on more than 3.5 million X-rays, xQR can reliably identify abnormalities in the lungs, pleura, mediastinum, and bones in under 1 minute for each scan, making it a reliable assistant in detecting malignant and non-malignant lung nodules, tuberculosis, and other lung diseases.
Image title: qXR chest X-ray reporting
Image source: qure.ai — AI for Chest X-rays
Lung cancer detection is a significant challenge for healthcare systems globally, with nearly 50% of patients diagnosed at stage 4, leading to poorer outcomes. By using Qure's AI solution to enhance the speed and accuracy of chest X-rays performed annually at NHSGGC, we can expedite further imaging and treatment, improving patient care throughout the pathway.
Benefits of AI in radiology
AI-driven radiology solutions for computer-aided diagnosis and workflow automation can substantially improve clinical outcomes, enhancing diagnostic accuracy, accelerating image interpretation, and reducing clinician workload.
Early detection
Improved prioritization
Improved accuracy
Reduced radiation exposure
Enhanced image quality
Faster diagnosis
Improved access to care
Improved reporting
Cost optimization
AI in radiology
AI can more reliably detect diseases at early stages, preventing complications and dramatically improving patient outcomes.
Automatically prioritizing scans based on case
severity, AI-based radiology tools provide decision
support, allowing clinicians to intervene early.
Most radiology AI tools can detect abnormalities
more accurately than human radiologists,
increasing patients’ chances for recovery.
AI systems help capture high-quality images on the
first attempt, reducing the need for repeat scans and
radiation exposure for patients and staff.
AI software can help improve the image quality of
medical scans, facilitating abnormality detection
and diagnostics.
AI solutions for radiology speed up the diagnosis
process, reducing the risk of clinician burnout and
allowing patients to receive treatment more quickly.
By improving patient throughput and accelerating
decision-making, AI-powered radiology tools can
democratize access to radiology.
Most AI-powered radiology tools automatically
produce error-free and standardized reports,
saving time and streamlining workflows.
Reducing human diagnostic error, AI radiology
solutions help clinics lower costs associated with
scan retakes and resource waste.
Common challenges with AI in radiology & potential solutions
Despite AI’s benefits, implementing AI-powered solutions for radiology can be challenging due to issues related to system alignment with clinical workflows, as well as IT infrastructure and training data quality. At Itransition, we help healthcare teams overcome the following and other potential implementation risks to facilitate smooth solution adoption.
Challenge | Solution | |
|---|---|---|
Aligning medical guidelines with AI outputs |
Most current AI applications in radiology provide estimates of how likely a certain patient is to have
complications based on radiological imaging. For example, an AI system concludes that a breast lesion of a
certain patient has a 10% chance of being malignant. A radiologist could opt for a biopsy, but the AI
system may not understand the severity of the problem and deem a 10% chance of cancer insignificant for
conducting a biopsy.
| Developers and medical professionals must work in close collaboration. Insights from medical professionals can improve the performance of AI-based image acquisition and processing and decision-support solutions. |
Poor IT infrastructure |
A major hurdle for AI adoption is poor IT infrastructure, which impedes AI implementation and prevents AI
tools from functioning effectively. Many providers struggle with siloed data, outdated security measures,
and a lack of system interoperability.
| Combating these problems comes down to gradual changes in IT infrastructure, preferably with support from an experienced third-party vendor. Healthcare organizations can start their AI adoption journey with the adoption of image management and PACS systems for improved image quality and easier retrieval. |
Data quality |
The lack of high-quality labeled datasets for training AI models is a universal problem across sectors and
industries, and radiology is no exception. Moreover, trained on narrow or biased datasets, AI systems can
bear algorithmic bias, leading to unfair or discriminatory outcomes.
| One of the main strategies for overcoming training data shortage is synthetic data generation. Data augmentation can also help diversify available training datasets. Instead of building AI models from scratch, healthcare institutions can also opt for foundation or pre-trained models. |
Want to add AI-based technologies into your radiology practice?
AI methods in radiology
AI has become synonymous with machine learning (ML), which encompasses many techniques used to create predictive models, including deep learning models. Today, the most popular way of mimicking human decision-making is artificial neural networks (ANN), which is a certain type of very flexible deep learning model.
ANN is inspired by the design of biological neural networks. In other words, this model tries to mimic the network of neurons that comprise the human brain so that it can solve problems similar to the way a human would.
Currently, the convolutional neural network (CNN), which is a class of ANN, is the most popular model for analyzing visual data, making it the most important component of AI in radiology.
Image title: CNN operation sample
Data source: journals.sagepub.com—Artificial intelligence in radiology: friend or foe? Where are we now & where
are we heading?
AI in radiology: implementation tips
Implementing AI in radiology requires careful planning and close collaboration between clinical and technical teams. Here are the best practices healthcare providers should consider to ensure project success and maximize solution ROI.
Build a multidisciplinary team | While input from field experts is always critical to the development of AI solutions in virtually any industry, medical imaging analytics calls for radiologists to have a much more decisive and significant role in the development of AI-based radiology solutions. Engineers and data scientists lack an understanding of essential radiological and anatomical concepts, which is crucial to creating AI systems applicable in radiology. |
|---|---|
Gain support from leadership | Healthcare providers should get key decision-makers and executive leadership on board with the implementation of AI tools. According to the latest report by Deloitte, healthcare providers who succeeded in AI initiatives put leaders at the core of AI transformation. These leaders should clearly communicate with the radiology department on how new tools will impact their daily workflows. |
Prioritize AI explainability | In healthcare, where decisions can directly influence one's well-being, it is paramount for radiologists to understand the underlying decision-making of AI systems. Most AI radiology tools use deep learning models, and system interpretability and transparency become crucial. While deciphering deep learning models is a universal task across many AI applications, developers are responsible for minimizing bias and variability and making models as explainable as possible. It comes down to continuous algorithm validation, dataset audit, evaluation of its performance metrics, standardized documentation, and routine bias assessment. |
Itransition’s AI services
With over 5 years of experience in AI software development and consulting, Itransition helps healthcare providers make AI-powered solutions a natural part of their daily workflows.
AI consulting
Itransition’s consultants help integrate AI and maximize its value in radiology settings, providing expert support at any stage of the AI implementation lifecycle. From business case development to AI project oversight, we ensure risk-free AI solution deployment and its reliable operation.
AI development
Itransition’s team delivers high-performing AI software for radiology that is fully aligned with a clinic’s needs and workflows, as well as industry standards and regulations. We deliver a wide range of solutions for different use cases, including AI agents, analytics software, and generative AI solutions.
About Itransition
Providing software development and consulting services for healthcare since 1998
Delivering HIPAA- and HITECH-compliant software solutions
Adherence to OWASP, IEC 62443, and other global and industry-specific security principles
Experience with healthcare industry standards and coding systems, including DICOM, FHIR, ICD-10, and CPT
Internal Healthcare and AI/ML Center of Excellence
Quality and information security management compliant with ISO 9001 and ISO 27001
Add AI into your radiology practice
AI-based tools are already used in different fields of radiology, and their role will increase moving forward. As large language models and other AI-related technologies evolve and become capable of handling more complex tasks, they will find novel applications within radiology settings. The validation of existing AI applications and development of new ones for solving common radiological problems requires effective collaboration between radiologists, data scientists, and engineers.
AI will make the next two decades transformational for healthcare and radiology in particular. Our professional AI consultants can help you implement AI-based technology into your radiology practice.
FAQs
AI is designed to augment the work of radiologists, not replace human intelligence. AI software handles repetitive tasks and data analysis, allowing human experts to focus on complex diagnostics and patient care.
With the growing acceptance of AI among medical professionals and regulatory bodies, this technology will take over radiologists’ repetitive tasks and become their essential reference tool. The key question is how AI will further enhance diagnostic accuracy and efficiency while maintaining human oversight.
The first use of AI in radiology dates back to 1992 and was used to detect microcalcifications in mammography. Arterys, the first AI-based FDA-approved medical imaging solution, was certified in 2017.
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