Key takeaways:
- Artificial intelligence is flipping the script on medical imaging analysis by improving image quality, accelerating diagnosis, and enhancing patient care.
- In medical imaging, AI is used for image reconstruction, medical image segmentation, workflow optimization, and anomaly detection.
- Challenges, including data privacy, clinical validation, and ethical implementation, impede widespread adoption of AI in medical imaging.
Radiologist shortages are making medical imaging backlogs grow bigger every day. According to the Journal of Clinical Imaging, up to 68% of radiology practices have unreported radiology exams. Even after 6 months, around 20% of brain and chest CTs remain unreviewed.
While the integration of AI in medical imaging can’t fill the workforce gap, it can at least ease the strain on existing professionals. But it’s not only about automation. AI medical imaging technology can rewire the approach to disease diagnosis by making it faster and more precise.
Key applications of AI in medical imaging
Artificial intelligence and machine learning are in the midst of their big moment in medical imaging. In 2025, the global AI in medical imaging market size is calculated at $1.67 billion and is expected to surpass $14 billion by 2034. Here’s where AI is making the biggest impact in the field.
Image acquisition and image reconstruction for improved analysis
Getting high-quality images often requires greater X-ray penetration, which equates to greater radiation doses or longer scan times. Either can put the patient’s health at risk. On the other hand, low-quality images can easily lead to missed diagnoses or false positives.
Trained on vast datasets of both low-quality and high-quality medical imaging, deep learning models, especially convolutional neural networks, enable low-dose imaging protocols that do not undermine image quality.
- Denoise and sharpen images to enhance details in scans acquired with lower radiation doses or faster protocols.
- Reconstruct higher-resolution images that were initially captured at a lower resolution (lower-resolution scans, portable ultrasound, mobile dermatology, etc.)
- Minimize artifacts caused by patient movement, metal implants, and other interferences.
AI can perform well even when there is a mix of different types of noise and degradation patterns, like artifacts paired with low contrast or motion blur.
Case in point: AI assistant for atopic patients
Accurate image analysis was a core component for Atopic, our client’s solution focused on assessing the severity of lesions with high precision. Built for iOS and Android, the app required a high-performing AI model to facilitate accurate analysis directly from a mobile device.
Our team integrated a custom AI model trained on over 10,000 images of atopic dermatitis-affected skin. The model lets users monitor skin condition over time through side-by-side photo comparisons and share those with physicians.
Early disease detection and diagnosis
Subtle anomalies in medical images, like a faint shadow on a mammogram or a small nodule on a lung CT, are not always visible to the radiologist’s eye. AI algorithms can double as readers and analyze medical images to spot what the human eye might miss, months before those conditions become hard to treat.
- In mammography, integrating AI into screenings сan improve early detection of breast cancers, reducing the number by 30%.
- In retinal imaging, AI algorithms for DME detection show the impressive sensitivity and specificity rates of over 95%, with the accuracy surpassing 0.98 when using AI to spot DME through OCT and/or fundus imaging modalities.
- A deep learning-based AI system can detect benign nodules, primary lung cancers, and metastatic lesions with respective accuracy of 94.3%, 96.9%, and 92.0%.
Thanks to advancements in large language models (LLMs), AI medical imaging solutions have also secured an edge in detecting rare diseases. Since LLMs are trained on enormous datasets that feature rare disease profiles, they can suggest to radiologists possible rare conditions that medical professionals may have never encountered before.
Case in point: Exo
Founded in 2015, Exo markets handheld AI-based medical imaging devices that aid medical examiners in doing ultrasound examinations via an app. The device employs Exo’s FDA-cleared SweepAI technology that guides image capture in real time while the company’s imaging platform applies AI indicators to determine conditions. Since the device simply plugs into a smartphone, it allows medical professionals to take medical imaging to remote or underserved areas.
Medical image segmentation and quantification
In their pure form, medical images are raw 2D slices or 3D models that contain all kinds of information. To distill clinically valuable insights, radiologists have to manually delineate regions of interest, like tumours, organs, blood vessels, and so on.
AI algorithms like CNNs and U-Net architectures relieve radiologists of time-consuming segmentation work by automatically labeling areas of interest within scans. Key applications of AI algorithms for image segmentation include:
- Tumour delineation and growth tracking — AI outlines tumors in MRI, CT scans, and PET scans and calculates the volume, shape, and texture to help healthcare professionals plan radiation therapy or monitor how the patient responds to treatment.
- Cardiac function analysis — Artificial intelligence identifies and classifies coronary artery plaques in CT angiography.
- Neurological analysis — Smart algorithms quantify hippocampal atrophy in MRI scans, making it possible to detect early-stage Alzheimer’s disease and Parkinson’s disease with over 90% accuracy.
Although traditional AI medical image algorithms demonstrate effectiveness, they are designed with a specific task in mind and require large quantities of annotated data to adapt to new scenarios.
Foundation models can generalize to new tasks and compensate for the downsides of traditional methods.
Case in point: GE HealthCare
Healthcare tech company GE HealthCare collaborated with Nvidia to develop a promptable foundation model for ultrasound image segmentation. Called SonoSAM1, the model is reported to beat other competing methods and requires from 2 to 6 clicks for precise segmentation.
Workflow optimization and triage
In busy radiology departments flooded with overused STAT labels, medical specialists scramble to find the bandwidth to deal with a relentless stream of studies. Artificial intelligence streamlines and partially automates the way radiologists work, so they can reclaim time for truly high-priority tasks.
AI-powered tools streamline radiology workflows in a variety of ways, including:
- Case prioritization — AI algorithms process scans in real time, flag urgent cases, and move them to the top of a radiologist’s queue.
- Workflow prioritization — Instead of flow coordinators and lead radiologists, care teams can get AI to automatically assign cases to the right subspecialty in real-time.
- Automated pre-reads and reporting — Augmented with natural language processing, imaging models can recognize fields on the image to generate preliminary reports and structure data.
- Quality control — Artificial intelligence double-checks scans, and if they’re incomplete or low-quality, the algorithm tags them for re-acquisition.
In emergency pathology, AI tools can detect life-threatening conditions on head CTs, chest X-rays, and other studies within minutes of scan acquisition, allowing doctors to initiate treatment faster.
Case in point: Aidoc
Aidoc is a clinical AI company focused on medical imaging solutions. Aidoc’s AI integrates with hospitals' PACS/RIS software to automatically scan every incoming head CT in under 30 seconds after a scan is completed. If an intracranial hemorrhage is detected, the AI pushes it to the top of the neuroradiologist’s worklist and immediately notifies the radiologist.
Integration with clinical decision support
When integrated with an AI-enhanced clinical decision support system (CDSS), imaging platforms create a closed-loop ecosystem. Imaging tools analyze scans and hand over structured findings to an AI-powered CDSS. The CDSS cross-references the findings with the patient’s electronic health records to provide additional clinical context and identify any inconsistencies that may have been overlooked.
Together, imaging platforms and CDSSs are a powerful duo that:
- Reduces false positives and negatives — Combined, imaging results and rich contextual data enable a CDSS to distinguish between clinically important insights and one-off anomalies. This way, healthcare professionals get fewer false alarms and fewer missed diagnoses.
- Informs differential medical diagnosis — A complete data view allows the CDSS to generate a ranked list of likely pathologies.
- Helps with treatment matching — In oncology, AI tools can align tumor imaging characteristics with genomic and molecular data to predict disease progression and suggest personalized treatment strategies.
Case in point: Nuance PowerScribe One
Nuance PowerScribe One is a pioneering radiology generative AI co-pilot that has made a dent in the market due to its unique real-time NLP dictation reconciliation. The tool can surface contextual CDS insights directly within the reporting interface. Also, the platform can easily integrate with third-party systems such as EHRs and PACS to make sure the AI-penned report factors in prior studies, lab results, and other patient data.
What are the key challenges and considerations in developing AI for medical imaging?
According to the survey, around two-thirds of U.S. radiology departments leverage AI in some capacity. However, with so much potential for AI-powered medical imaging to streamline radiologist workflows, healthcare stakeholders need an end-to-end implementation strategy to develop AI that meets clinical requirements.
Data acquisition, quality, and bias mitigation
To train deep learning algorithms for medical imaging, developers need large, comprehensive datasets. This requirement goes counter to the reality of typically scarce, highly specialized datasets in biomedical imaging. Also, labeling those scant few datasets is a challenge in itself, as annotation is time-consuming and expensive due to the involvement of clinical experts.
Data bias is another story. AI solutions that run on models informed by demographic shortcuts have a high probability of producing skewed predictions across subpopulations. To address the triple challenge of training data, healthcare software developers should:
- Gather diverse datasets for model training — Development teams can collaborate with hospitals, imaging centers, and other organizations to diversify training data.
- Prepare data for AI — Standardizing imaging protocols, resolutions, and metadata is an important lead-in step that lays the ground for a uniform training environment.
- Use data augmentation techniques or leverage synthetic data — To compensate for scarce data, developers can employ rotation, flipping, and other augmentation techniques, or utilize tools like generative adversarial networks to fill the gaps with synthetic data.
Validation and real-world performance
Benchmark datasets are designed for controlled research. It means that an AI model may struggle to adapt to real-world clinical environments that differ from the default conditions of the benchmark. Healthcare developers can solve this generalization problem by testing the model across sites and populations.
Before a full-scale deployment, healthcare innovators can also validate AI model performance with a silent mode deployment. In this case, the model shadows human clinicians and processes real-time patient data, but its outputs neither influence clinical decisions nor become visible to patients.
Regulatory compliance and ethical implementation
In the US, radiology artificial intelligence for diagnostic imaging falls into the Software as a Medical Device (SaMD) category. In the EU, medical imaging AI is governed under the EU Medical Device Regulation (MDR). Although these regulations have different approaches to classification and risk, they both mandate evidence of safety, clinical efficacy, and risk management of the tool.
To meet regulatory requirements and get the approval to market the software, healthcare innovators must take a comprehensive approach to development, including:
- Aligning the development and documentation rigor with the regulatory pathway of the software.
- Implementing a Quality Management System that complies with standards such as ISO 13485.
- Providing sound clinical evidence that demonstrates the safety and effectiveness of the tool.
- Developing a strategy for ongoing post-market monitoring, incident reporting, and more.
Data privacy and ethical aspects
As we’ve mentioned, large and diverse multi-centre datasets are vital to the model’s generalizability. However, this requirement conflicts with HIPAA, GDPR, and other data privacy requirements, because centralizing this data in one place exposes it to risks.
Federated learning, which allows the AI algorithm to learn from data locally, can be a solution to the privacy problem. Keep in mind that this technique alone doesn’t guarantee full data security, so it’s important to combine it with data anonymization, differential privacy, and other elements.
Closely linked to data privacy are ethical aspects around how patient data is handled. Informed patient consent to data used by AI, equitable access to AI capabilities, and fairness of the AI model are the three building blocks of building patient trust towards AI.
Workflow integration and clinician adoption
To become a part of the clinical practice, AI tools must plug into existing workflows without disrupting them or making them overly complex. Achieving that effect requires developers to build AI medical imaging solutions with healthcare data standards in mind (HL7, FHIR) and secure APIs. This will enable the solution to work in tandem with PACS, EHR, RIS, and other systems.
Alongside technical compatibility, AI-enabled medical imaging tools must also have user-friendly, intuitive interfaces that clearly display results in a way that supports the current clinical workflow. Also, radiologists need the last say when it comes to AI-powered medical diagnosis, so healthcare innovators need to integrate human-in-the-loop guardrails to give human clinicians the ultimate authority.
Future technology trends transforming medical imaging
Thanks to advancements in generative AI and multimodal models, medical imaging continues to evolve. From synthetic datasets to virtual 3D reconstructions, AI will keep on redefining the way healthcare providers visualize, understand, and diagnose.
Foundation models and multimodal AI
- Pre-trained multimodal models such as BioGPT, MedCLIP, LLaVA-Med, and IBM’s MultiMed are being adapted to the medical imaging field, enabling models to correlate radiological images with unstructured clinical notes for richer diagnostics.
- Google’s Med-PaLM can process and correlate cross-specialty data across medical imaging, genomics, sensor data, and text to improve diagnostic accuracy for rare diseases.
- These models require minimal labeled training data and can adapt to various modalities, including CT, MRI, and others.
Synthetic data and digital twins
- When real training data is scarce or too sensitive to be leveraged, AI-generated synthetic imaging data can provide artificial but realistic medical data to train or test AI models.
- Digital twins create patient-specific virtual models that allow healthcare professionals to simulate and predict patient outcomes, whether it’s disease progression modelling, treatment response forecasting, or another type of prediction.
- Together, synthetic data and digital twins create a closed self-reinforcing loop and a safe testbed for AI development, allowing developers to train and refine AI with no patient safety concerns.
Real-time and point-of-care imaging AI
- Lightweight AI models are being integrated into handheld ultrasounds, mobile X-rays, and wearable sensors for instant analysis in ambulances, rural clinics, and bedside settings.
- For low-resource settings, on-device processing enables AI to do analysis directly on the medical device, without sending the data to the internet.
Create AI-powered medical imaging solutions with Orangesoft’s expertise
The integration of artificial intelligence into medical image analysis unfolds a new chapter in the healthcare industry, one where it enhances diagnostic accuracy, supports early detection, and helps identify early signs of disease that may escape the human eye. Whether it’s classifying breast lesions, analyzing brain images, or handling scan data management, computer vision and machine learning algorithms are redefining what’s possible in medical practice.
Even so, AI is not meant to replace radiologists. It’s designed to be a force multiplier, helping radiologists shoulder the burden.
If you’re looking to make AI a part of your medical imaging solutions, our team can help you do just that — from model design to system integration and complementary software development. Get in touch with us to schedule a free intro consultation.