Building Custom AI Solutions for Radiology Imaging

Artificial intelligence is reshaping the field of radiology by introducing new opportunities for improving diagnostic accuracy and efficiency. With AI’s ability to analyze medical images, radiologists can detect conditions earlier, make more informed decisions, and ultimately provide better patient care. The true strength of AI lies not only in its automation capabilities but in how it empowers healthcare professionals with insights that were previously beyond reach.

However, implementing AI solutions in radiology is not a one-size-fits-all approach. Custom development is necessary to meet the unique needs of healthcare providers, ensuring the technology integrates smoothly into existing systems and amplifies the radiologist’s capabilities. These tailored solutions help optimize processes, reduce errors, and speed up diagnoses, all while maintaining the highest quality of care.

In this blog, we’ll dive into how to build custom AI solutions for radiology imaging that improve diagnostic accuracy, streamline workflows, and ultimately lead to better outcomes for patients.

How Can Custom AI Improve Image Processing in Radiology?

AI has made a significant impact in radiology, offering tools that promise quicker reads, fewer errors, and improved workflow. However, while off-the-shelf AI solutions work well for standard cases, they often fall short in real-world clinical environments. Here’s why custom AI solutions provide a better fit and can truly enhance your practice.

1. Poor Integration with Existing Systems

Most hospitals rely on PACS, RIS, or DICOM workflows, but many off-the-shelf AI tools operate as standalone applications. This forces radiologists to manually upload studies, switch between platforms, and handle time-consuming data transfers, all of which disrupt their workflow.

Custom AI Solution: Custom AI solutions integrate smoothly with existing systems, running directly within your current radiology viewers (e.g., Epic, Sectra), so your team can stay within their usual workflow.

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2. Inaccurate for Niche or Complex Cases

Generic AI tools are often trained on broad, generalized datasets. This means they can struggle with rare conditions, uncommon imaging protocols, or diverse patient populations. For example, an AI tool trained primarily on European patient data may overlook key diagnoses in regions with distinct demographic profiles.

Custom AI Solution: Custom AI models can be tailored to use your institution’s specific data, ensuring more accurate results for patient demographics, rare conditions, and unique imaging protocols.

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3. Lack of Scalability

Pre-built AI tools are often rigid. They can’t adapt to new equipment, changing guidelines, or hospital-specific workflows. This can lead to issues as your facility grows or technology evolves.

Custom AI Solution: Custom AI is built to scale with your practice. Whether you’re upgrading to new scanner models or adopting new imaging modalities like 3D mammography, a custom solution can grow alongside your needs.

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When Does Custom AI Make Sense?

A custom AI solution is ideal for practices that face:

• Frequent workarounds to integrate AI into workflows.
• High false positives or negatives, especially with rare or complex cases.
• Plans to expand or update imaging technology (e.g., adding PET-MRI).

Custom AI adapts to your specific needs, providing greater accuracy, smoother integration, and scalability that off-the-shelf solutions often can’t match.

Key Market Takeaways for AI in Radiology Imaging

According to Grand View Research, the global market for AI in medical imaging was valued at USD 1.01 billion in 2023 and is expected to grow at a rapid pace, with a projected CAGR of 34.8% from 2024 to 2030. This growth is driven by the increasing demand for processing complex medical data, the rising workload on radiologists, and the growing prevalence of chronic diseases requiring advanced imaging technologies for diagnosis and management.

Source: Grand View Research

AI tools in radiology are already making a significant impact. For instance, Viz LVO offers AI-powered stroke detection, while Lunit INSIGHT MMG aids in identifying dense breast tissue for more accurate breast cancer diagnosis. 

qXR by Qure.ai focuses on detecting chest pathologies, such as lung cancer and tuberculosis, with remarkable sensitivity and accuracy. These technologies are transforming the way radiologists interpret medical images, making diagnoses faster and more reliable.

Strategic collaborations are also driving innovation in this space. For example, Radiology Partners has partnered with RADPAIR to develop AI-powered reporting tools. This collaboration blends RADPAIR’s AI technology with RP’s expertise to address capacity challenges and enhance diagnostic accuracy, ultimately improving patient care across the U.S.

Collecting the Right Data for Radiology AI Algorithm

Building an effective AI model for radiology starts with gathering high-quality, clinically relevant data. Medical imaging algorithms demand a more rigorous data curation process than general AI projects to ensure reliability and compliance. Here’s how to collect the right data for a successful radiology AI model:

1. Define Your Use Case Clearly

Before diving into data collection, it’s crucial to define your use case in detail. Start by understanding the clinical problem you’re solving and what kind of output you need from the AI model.

• Clinical Problem: Identify the specific disease or condition the AI model will assist with, such as lung nodule detection, identifying brain hemorrhages, or triaging fractures.
• Output: Determine what the model will output, whether it’s classifying the presence or absence of a condition, segmenting specific areas of an image, or providing anomaly scores to prioritize cases.

For example, a breast cancer screening model needs mammogram images that are labeled with biopsy-proven malignancies, while a pneumonia detection system needs chest X-rays with radiologist-confirmed findings. 

Defining your use case upfront will ensure your dataset meets the exact needs of the algorithm, preventing wasted resources on data that doesn’t support the desired outcome.

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2. Prioritize Data Heterogeneity

To create a model that performs well in real-world settings, your dataset should reflect the diversity found in clinical environments. Including images from various sources and ensuring the dataset is varied will help your model generalize better to new, unseen data.

• Scanner Diversity: Include data from different medical imaging machines (e.g., Siemens, GE, Philips) and models. This variation ensures the AI algorithm performs consistently across multiple types of equipment.
• Protocol Variations: Medical imaging involves numerous protocols and techniques. Ensure that your dataset includes contrast/no-contrast CT scans, a variety of MRI sequences (e.g., T1/T2), and different X-ray settings (e.g., kVp/mA settings).
• Patient Demographics: To prevent bias, include data from a wide range of patients in terms of age, gender, ethnicity, and medical conditions. This helps the model avoid skewed predictions and ensures it performs fairly across diverse patient populations.

Collaborating with hospitals or using public datasets like NIH’s ChestX-ray or RSNA can help fill these gaps and ensure the data is more representative.

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3. Image Quality: Mimic Real Clinical Conditions

It’s essential to ensure that the images used for training are representative of what the model will encounter in real clinical settings. This means including images that reflect common real-world conditions, such as motion artifacts, low-dose scans, or minor positioning errors.

• “Good Enough” Images: Avoid selecting only the “perfect” images. The AI should be trained to handle imperfect data, as real clinical scans can often have noise, motion blur, or suboptimal quality due to factors like patient movement.
• Exclude Non-Diagnostic Images: Images that are highly distorted or non-diagnostic (e.g., due to severe artifacts) should be excluded from the dataset.
• Standardize Preprocessing: Ensure images are processed consistently—this includes standardizing image resolution, orientation, and DICOM metadata to match clinical workflows.

Over-curating your dataset by excluding imperfect scans may lead to models that perform well in testing but fail in real-world environments. The key is to balance quality with authenticity.

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4. Data Quantity: How Much Is Enough?

The quantity of data needed for a successful AI model varies depending on the complexity of the task. More complex problems or tasks that require pixel-level segmentation need significantly more data than simpler binary classification tasks.

• Binary Classification (e.g., detecting pneumothorax): A minimum of 1,000–5,000 images is needed, but the ideal number would be over 10,000.
• Rare Findings (e.g., pediatric tumors): A smaller number of images (500+) may be sufficient, but using data augmentation techniques can help expand the dataset. Ideally, 2,000+ images are preferred.
• Pixel-Level Segmentation (e.g., organ delineation): For tasks requiring precise segmentation, you’ll need at least 500 annotated scans, with 5,000+ being ideal.

Keep in mind that deep learning models tend to require exponentially more data than traditional rule-based algorithms. A larger dataset helps the model better generalize and detect more subtle patterns. Additionally, federated learning is a potential option to pool data from multiple institutions while maintaining privacy and security.

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5. Label Quality: The Make-or-Break Factor

High-quality labels are crucial for training a reliable AI model. Garbage-in-garbage-out is especially true in medical imaging, where inaccurate or inconsistent labeling can lead to poor performance. Ensure the following when labeling data:

• Clinically Accurate Labels: Always rely on board-certified radiologists or specialized medical professionals to label your data, rather than using crowd workers who may lack the necessary expertise.
• Consensus-Driven Labels: When there is disagreement between annotators, resolve it through panel review or a consensus approach to ensure consistency and accuracy.
• Standardized Labeling: Use established clinical guidelines (e.g., LI-RADS for liver lesions, BI-RADS for mammography) to ensure your labels are standardized and aligned with clinical practices.

Steps for Developing a Custom AI Solution for Radiology Imaging

Here are the steps to develop a custom AI solution for radiology imaging,

1. Define the Use Case and Objective

Start by clearly identifying the problem the AI solution will solve, whether it’s automating image analysis, improving diagnostic accuracy, or streamlining workflows. Collaborate with radiologists to pinpoint the conditions and types of images the system will focus on, ensuring the AI addresses real-world challenges in radiology.

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2. Data Collection and Preparation

The next step is gathering high-quality, annotated images that will teach the AI model to identify specific conditions, like tumors or fractures. This data can come from hospitals, research institutions, or publicly available medical datasets. It’s crucial to preprocess this data to make sure the model is trained effectively.

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3. Select the Right AI Models and Algorithms

Choosing the correct algorithms is key to ensuring the AI system works effectively. CNNs are great for image recognition, while models like U-Net excel at segmenting specific areas of images. Picking the right model helps the system provide accurate results in real-time.

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4. Model Training and Evaluation

With the data in place, the model is trained on powerful computing systems, often using GPUs, to process and learn from the images. After training, it’s essential to evaluate the model’s performance using metrics like accuracy and sensitivity. This step ensures the model is ready to be deployed for real-world use.

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5. Iterate and Fine-Tune the Model

After initial training, fine-tuning is crucial to optimize the model’s performance. This involves tweaking hyperparameters, employing techniques like transfer learning, and testing the model on fresh data to ensure it works well across different medical scenarios.

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6. Integration with Radiology Systems 

Integrating the AI system with existing radiology infrastructure, such as PACS (for image storage) and EHR (for patient data management), ensures smooth operation within the clinical environment. This makes it easy for radiologists to access and review AI-generated insights alongside other patient information.

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7. Implement Explainable AI Features

For radiologists to trust AI, the system must explain its decisions. By using techniques like saliency maps, the AI can highlight the areas of the image that influenced its diagnosis. This transparency helps healthcare professionals feel more confident in the AI’s recommendations.

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8. Compliance with Healthcare Regulations

To ensure the AI solution is legally and ethically sound, it must meet healthcare regulations like FDA approval in the U.S. or CE marking in Europe. Additionally, the system needs to comply with data privacy laws such as HIPAA to protect patient information.

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9. Pilot Testing and Clinical Validation

Before full deployment, the AI system undergoes pilot testing in real-world clinical settings. This allows radiologists to evaluate the tool’s performance and provide feedback. Based on their input, the system is fine-tuned to fit clinical workflows better and improve its diagnostic capabilities.

Cost of Developing a Custom AI Solution for Radiology Imaging

The cost of developing a custom AI solution for radiology imaging varies based on the complexity of the algorithms, data requirements, and the level of integration with existing systems.

Stage	Subcomponent	Description	Estimated Cost
1. Research & Planning	Clinical Needs Assessment	Define radiology workflow issues with stakeholder input	$500 – $2,000
	Feasibility Study	Assess technical/data feasibility with AI experts	$1,000 – $3,000
	Defining Scope & Objectives	Define anatomical regions, tasks, and objectives	$500 – $1,000
	Initial Team Formation	Early consultation with data scientists, radiologists, developers	$2,000 – $4,000
	Subtotal		$1,000 – $10,000
2. Data Preparation & Annotation	Data Acquisition & Curation	DICOM collection, anonymization, pipeline setup	$500 – $5,000
	Annotation (Radiologists or Tools)	Labeling images for AI (lesions, organ outlines, etc.)	$1,500 – $25,000
	Subtotal		$2,000 – $30,000
3. Backend & AI Model Development	Infrastructure Setup	Cloud setup, storage, compute environment	$500 – $5,000
	AI Model Selection & Development	Fine-tuning pretrained CNNs or segmentation models	$2,000 – $20,000
	Model Training & Validation	Model iteration, testing, and performance tuning	$500 – $15,000
	Subtotal		$3,000 – $40,000
4. Frontend & UI/UX Development	UI/UX Design	Wireframing, design mockups, interactive prototypes	$1,000 – $5,000
	Frontend Development	Web portal or integration plugin development	$1,000 – $15,000
	Subtotal		$2,000 – $20,000
5. Core Features	Image Viewing & Analysis Tools	Tools to display AI results (bounding boxes, masks, confidence scores)	Included in Backend/UI
	Reporting & System Integration	Basic reports, API integrations (if applicable)	Included in Backend/UI
	Authentication	Basic login, user access control	Included in Backend/UI
	Subtotal		(Included Above)
6. Testing & QA	Functional Testing	Verify feature accuracy and logic	$500 – $3,000
	Performance Testing	Check app speed, stability, cloud efficiency	$500 – $2,000
	Usability Testing	Interface testing by radiologists	$500 – $2,000
	Internal Clinical Validation	Limited real-world validation (not full trials)	$1,000 – $3,000
	Subtotal		$1,000 – $10,000
TOTAL ESTIMATED COST			$10,000 – $100,000

Factors Affecting the Cost of Developing Custom AI Solutions for Radiology Imaging

Several factors impact the cost of developing AI solutions for radiology imaging. Here are the key variables to consider:

Complexity of the AI Task

The complexity of the problem the AI is solving affects costs. Simple tasks, such as image classification, are less expensive to develop than complex ones, like multi-organ segmentation or predicting disease progression.

Volume and Quality of Training Data

The amount and quality of training data have a significant impact on costs. Larger datasets require more storage and processing power, while poor-quality data necessitates additional time for cleaning and preparation.

Annotation Requirements

Expert annotation is crucial in the development of radiology AI. The cost depends on the number of images that need annotation and the complexity of the abnormalities. More detailed annotations are more expensive.

Computational Resources

Training AI models requires powerful computing resources, especially for large datasets. The need for high-performance GPUs and cloud services, combined with the time spent on training, contributes to the overall cost.

Proven Tips for Successfully Implementing AI in Radiology Practice

Artificial Intelligence is reshaping radiology workflows, but to truly benefit from it, successful implementation requires more than just installing software. Here are eight actionable strategies that leading institutions have used to integrate AI into their practice while maximizing its value seamlessly.

1. Engage Non-Imaging Clinicians Early

AI doesn’t just impact radiologists; it also changes how clinicians, such as ER doctors, oncologists, and surgeons, interact with imaging results. The key to success is involving these clinicians early in the process.

Best Practices:

• Offer training sessions to non-radiologist clinicians on how AI-enhanced reports work. Highlight how AI can prioritize urgent cases, such as stroke alerts arriving faster.
• Address any concerns about AI by explaining that AI assists radiologists, but doesn’t replace their expertise or clinical judgment.

Example: At Mass General, AI-powered pulmonary embolism alerts reduced ER wait times by notifying clinicians directly through EHR pop-ups, enabling quicker decision-making.

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2. Educate Patients About AI’s Role

Patients should feel confident in the technology being used in their care. Transparency and clear communication are essential for building trust.

Best Practices:

• Explain how AI improves diagnostic accuracy and efficiency. For example, let patients know, “AI double-checks your mammogram for even the smallest calcifications.”
• Simplify the language in patient-facing documents. A sample FAQ could be: “Your scan was reviewed by both a radiologist and an AI tool to ensure nothing was overlooked.”

This transparency reassures patients and makes them feel more comfortable with AI’s role in their care.

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3. Delegate Tasks to Radiographers & Nurses

AI can significantly improve efficiency by taking over routine tasks, allowing radiologists to focus on more complex diagnoses. Radiographers and nurses can be trained to handle some of these tasks.

Best Practices:

• Use AI to pre-screen studies, such as flagging chest X-rays with potential fractures or abnormalities, and prioritize these cases.
• Allow radiographers and nurses to assist with preliminary measurements, such as tumor sizes or coronary calcium scores, before they are passed to the radiologist for review.

Limitation: Remember that final diagnoses should always come from qualified physicians, as outlined by professional guidelines.

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4. Prioritize Vendors with Strong Support Teams

The success of AI adoption depends not only on the technology itself but also on the level of support provided by the vendor. Without reliable support, even the best AI solution can become a burden.

Best Practices:

• Choose vendors that offer 24/7 troubleshooting, as issues can arise outside regular working hours.
• Ensure the vendor provides dedicated, hands-on training for your team, rather than just pre-recorded webinars.

Red Flags: Be cautious of vendors who can’t offer clear service-level agreements, references from similar-sized hospitals, or a reliable track record of customer support.

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5. Understand AI’s Unique Outputs

AI models generate outputs that differ from traditional radiologist interpretations, so it’s important to understand what these results mean in context.

Best Practices:

• AI might highlight areas of interest in the form of heatmaps or confidence scores (e.g., “87% likelihood of malignancy”), which need to be interpreted carefully.
• Ensure that AI outputs are accompanied by clear explanations that help clinicians understand the context and limitations of the findings.

Solution: Our AI models include user-friendly reports that explain why certain findings may lead to false positives, such as near rib crossings, helping radiologists make more informed decisions.

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6. Continuously Monitor and Refine AI Performance

Once AI is integrated into your radiology practice, it’s important to monitor its performance regularly and make adjustments as needed. AI models can drift over time, especially as new data becomes available or patient demographics shift.

Best Practices:

• Establish a system to continually assess the accuracy of AI predictions, especially in complex cases or emerging conditions.
• Encourage feedback from radiologists and clinicians about the AI’s performance, and use that feedback to refine the model.

Pro Tip: Regularly retraining AI models with new clinical data ensures that the system remains accurate and relevant, providing continuous improvements to the practice.

Top 5 AI Softwares for Radiology Imaging in the USA

Here are the top five AI software solutions in the USA, each bringing unique capabilities to the field and helping radiologists deliver better care.

1. Aidoc

Aidoc’s AI platform, aiOS™, integrates seamlessly with existing radiology systems, including EHR, PACS, and scheduling tools. Its real-time triage algorithms can quickly detect and prioritize critical conditions such as intracranial hemorrhages or pulmonary embolisms. The platform’s intuitive interface consolidates results from multiple algorithms, reducing cognitive burden and boosting efficiency. Aidoc is used by leading hospitals, including Cedars-Sinai, to expedite decision-making and improve patient outcomes.

2. Enlitic

Enlitic uses deep learning to improve the accuracy and speed of radiological interpretations across X-rays, CT scans, and MRIs. The platform’s ENDEX solution standardizes imaging data and streamlines workflow, while ENCOG de-identifies medical images to protect patient privacy. Enlitic has been shown to help radiologists read cases 21% faster, improving throughput and reducing errors. Its ability to map DICOM metadata and automate data routing makes it an ideal solution for high-volume imaging environments.

3. Rad AI

Rad AI focuses on automating routine tasks for radiologists, particularly in reporting and follow-up management. The Rad AI Impressions tool generates automated, guideline-based radiology reports, with 90-95% of impressions requiring no edits. This level of accuracy reduces radiologist burnout and increases report consistency. Additionally, Rad AI automates follow-up management, ensuring timely patient care and reducing missed recommendations, especially in busy, high-acuity settings.

4. Butterfly Network

Butterfly Network’s iQ ultrasound device is a groundbreaking portable imaging tool that leverages ultrasound-on-chip technology. The device provides real-time imaging and AI-driven guidance, making it ideal for point-of-care settings, such as emergency rooms or remote locations. Recently FDA-cleared for its iQ3 model, Butterfly Network offers a cost-effective, portable ultrasound solution that enables quick and accurate diagnostics wherever needed.

5. Subtle Medical

Subtle Medical enhances the quality of low-dose or noisy medical images, enabling radiologists to interpret high-resolution scans while using less radiation or reducing scan times. This is especially useful for patients who may struggle to remain still during long imaging procedures. By improving image clarity and reducing the need for repeat scans, Subtle Medical’s AI algorithms help boost radiologist confidence and enhance patient safety.

Conclusion 

Developing custom AI solutions for radiology imaging provides healthcare businesses with a powerful means to enhance diagnostic accuracy, streamline workflows, and improve patient care. By automating image analysis and identifying potential issues early, AI reduces the risk of human error, allowing radiologists to focus on more critical tasks. 

For healthcare businesses, investing in AI for radiology imaging not only boosts efficiency but also leads to cost savings and better patient outcomes. By adopting these solutions, healthcare providers can stay competitive, deliver faster diagnoses, and position themselves as leaders in the evolving healthcare technology landscape.

Looking to Develop Custom AI Solutions for Radiology Imaging?

At Idea Usher, we specialize in developing AI-driven tools that enhance diagnostic precision and streamline radiology workflows. With over 500,000 hours of coding experience, our team of former MAANG/FAANG developers is ready to bring your vision to life with high-quality, scalable solutions. 

Explore our latest projects to gain insight into the exceptional work we can deliver for you, helping you stay ahead in the competitive healthcare technology landscape.

FAQs

Q1: How to develop a custom AI solution for radiology imaging?

A1: To develop a custom AI solution for radiology imaging, start by gathering high-quality medical images and data to train the AI models. Work closely with radiologists to understand the specific needs and challenges, then select the appropriate AI algorithms, such as deep learning for image recognition. Design the system to integrate seamlessly with existing imaging tools and ensure compliance with medical regulations. Finally, the system must be tested thoroughly for accuracy and reliability before deployment, and the system must be refined with feedback from healthcare professionals.

Q2: How can AI be used in radiology?

A2: AI in radiology can be used to automate the analysis of medical images, such as detecting tumors, fractures, or other abnormalities in X-rays, MRIs, and CT scans. AI can enhance diagnostic accuracy by identifying patterns that the human eye might miss, speed up the review process, and assist radiologists in prioritizing cases. It also helps streamline workflows by reducing the time spent on routine image assessments, allowing radiologists to focus on more complex cases.

Q3: Will AI replace radiologists?

A3: AI is unlikely to replace radiologists entirely, but it will significantly enhance their capabilities. AI can automate repetitive tasks, speed up image analysis, and provide valuable insights, but human expertise is still crucial for interpreting complex cases and making final diagnostic decisions. Instead of replacing radiologists, AI serves as a powerful tool that supports and augments their work, improving accuracy and efficiency.

Q4: What is the cost of developing a custom AI solution for radiology imaging?

A4: The cost of developing a custom AI solution for radiology imaging depends on factors such as the complexity of the algorithms, the amount of data required, and the level of integration with existing systems. Additionally, ongoing maintenance, model updates, and compliance with medical regulations can influence the overall cost. While developing a high-quality, tailored AI solution may involve significant investment, the long-term benefits in terms of improved diagnostic efficiency and reduced errors can make it a valuable asset for healthcare providers.