Did you know that over 3.6 billion diagnostic imaging procedures are performed globally each year? As this number continues to rise, radiologists face increasing demands, more images, tighter deadlines, and growing exhaustion in interpretation. This is why hospitals and healthcare systems have started piloting AI in radiology.

Radiology sits at the heart of modern medicine, helping doctors diagnose everything from fractured bones to brain tumors. AI is transforming radiology from the ground up, making image interpretation faster, more accurate, and more accessible. In this blog, we’ll explore how AI is changing radiology, the key areas of impact, the challenges to adoption, and what the future holds.

Can labs use AI in radiology?

Yes, AI in radiology offers compelling value. Here is how.

Radiology generates a huge volume of data. Every X-ray, CT scan, MRI, and ultrasound produces complex images that require expert interpretation. AI thrives in high-data environments, especially when patterns and anomalies need to be identified.

To elaborate, these are the key factors that make AI in radiology an important tool:

• Data-rich domain: Medical imaging generates millions of annotated examples, perfect for training AI.

• Pattern recognition problem: Radiologists are, essentially, expert pattern recognizers. AI excels at this task, especially deep learning and with purpose-built ML models like RetinaNet.

• High workload: There continues to be a global shortage of radiologists. AI assistants for radiologists will ease the burden.

• Precision requirements: Minor errors can lead to missed diagnoses. AI can offer a second pair of eyes, improving accuracy.

“At GoML, we’re helping clinics and diagnostic centers harness the power of AI to turn radiology data into real-time, actionable insights, improving accuracy, reducing workload, and ultimately enhancing patient care,” notes Prashanna Rao, Head of Engineering, GoML.

How are labs using AI in radiology today?

1. Image interpretation and diagnosis

AI models, especially convolutional neural networks (CNNs), can now interpret medical images with high accuracy. Trained on thousands or millions of annotated scans, these models can detect abnormalities such as:

• Lung nodules on chest X-rays

• Intracranial hemorrhage on head CTs

• Breast cancer on mammograms

• Fractures in bones

• Lesions in lungs

For example, Google Health’s AI system achieved radiologist-level performance in detecting breast cancer from mammograms, reducing false positives and false negatives. Similarly, Aidoc and Zebra Medical Vision offer FDA-approved AI tools for identifying strokes, pulmonary embolisms, and more.

2. Triage and workflow prioritization

AI can automatically scan incoming images and flag critical cases for immediate review, such as stroke, hemorrhage, or pneumothorax, allowing radiologists to prioritize life-threatening conditions.

This improves patient outcomes through faster interventions and streamlines radiology departments’ workflows.

3. Quantitative imaging and measurement automation

AI can take over tedious measurement tasks, like calculating tumor size or lung volume, which are essential for monitoring disease progression.

This ensures consistency, reduces human variability, and saves radiologists time, allowing them to focus on more interpretatively complex tasks.

4. Radiomics and predictive modeling

Radiomics is all about extracting large amounts of features from medical images, like texture, shape, and intensity, that are invisible to the naked eye.

AI can analyze these features to:

• Predict disease progression (e.g., tumor aggressiveness)

• Correlate image features with genetic data (radiogenomics)

• Support personalized treatment planning

This paves the way for precision medicine, where imaging insights help guide targeted therapies.

5. Reporting assistance and structured reports

AI-powered Natural Language Processing (NLP) tools can assist in generating structured radiology reports, reducing dictation time, and ensuring clarity.

Some systems even suggest possible diagnoses and standardize language consistency across reports, especially useful in multi-hospital settings.

What are the key challenges to the adoption of AI in radiology?

Despite the promise and proven results from using AI in radiology, there are challenges.  Key challenges include:

• Data privacy and regulatory compliance: Health data is sensitive. Compliance with HIPAA (US), GDPR (Europe), and other laws act as a drag on speed.

• Integration with PACS/RIS systems: AI tools must integrate seamlessly into existing Picture Archiving and Communication Systems and workflows.

• Model generalizability: AI models trained on one hospital’s data may not perform well elsewhere.

• Clinical trust and acceptance: Radiologists need to trust AI recommendations, which requires transparency in how models make decisions (explainability). Building trustworthy AI systems requires AI guardrails and governance frameworks.

• Cost and ROI clarity: Hospitals need a clear business case for investing in AI, tied to efficiency, cost savings, or improved outcomes.

Want to understand how global AI regulation is evolving and what it means for healthcare AI? Read our in-depth guide on AI regulation and liability.

What are the real world use cases for AI in radiology?

1. Image analytics and insights

AI models (like CNNs and vision transformers) are used to detect abnormalities in X-rays, CT scans, MRIs, and ultrasounds. See how we applied this in retinal image analysis for diagnostic precision. These tools assist radiologists by identifying lesions, fractures, tumors, and more, improving speed, accuracy, and consistency in diagnosis.

2. Gen AI powered automated report generation

Generative AI helps auto-draft structured radiology reports based on image findings, reducing dictation time, and improving consistency. Especially useful for routine exams like chest X-rays and follow-ups, AI enhances workflow efficiency and reduces radiologist fatigue.

3. Data processing and predictive analytics

AI leverages imaging and clinical data to predict disease progression (e.g., tumor growth), treatment response, or patient risk scores. These predictive insights help radiologists and clinicians personalize care and anticipate complications early.

4. NLP powered data analytics and insights

NLP algorithms analyze unstructured data from past radiology reports and notes to extract trends, identify care gaps, and support population-level decision-making. This is especially useful for hospital audits, research, and outcomes tracking.

5. AI for opportunistic screening in routine CT scans

AI is increasingly used to extract incidental but clinically important findings from routine CT scans, a practice known as opportunistic screening. For example, while a CT scan may be ordered for abdominal pain, AI algorithms can simultaneously analyze bone density to screen for osteoporosis, coronary calcium for cardiovascular risk, and metabolic disease indicators, all without additional imaging or cost.

6. Multimodal AI for diagnostic decision support

Multimodal AI combines imaging data with clinical records, lab results, and genomics to offer deeper diagnostic insights. For instance, Google DeepMind’s models integrate chest X-rays and EHRs to predict patient deterioration, while NVIDIA’s MONAI platform supports oncology diagnostics by merging scans with pathology data.

7. Gen AI for synthetic radiology data generation

Generative AI creates synthetic radiology images, like CTs and MRIs, for training models in rare or privacy-sensitive scenarios. GoML, for example, has a boilerplate for generating synthetic data.  

8. Radiology LLM agents for cross-specialty collaboration

LLM-based agents simulate collaboration between specialties, enabling a "Radiologist Agent" to consult with "Oncologist" or "Pathologist" agents. Agents built on OpenAI, Claude, or Amazon Bedrock pilots are being tested for AI-assisted tumor boards, offering combined insights across multiple modalities.

9. Explainable AI for radiologist trust and audits

Explainable AI improves trust by showing heatmaps, similar cases, and confidence scores alongside flagged anomalies. Tools like Aidoc and Annalise.ai embed visual overlays on scans, while Zebra Medical Vision adds case-based reasoning to justify findings and reduce false positives.

AI in radiology: Trends to watch in 2025

1. Generative AI for radiology report generation

Foundation models enable radiology to move toward fully AI-generated, editable reports, especially in routine and follow-up cases.

2. Edge AI and embedded diagnostics

AI inference is now happening on-device (portable scanners, wearables) with low latency, thanks to advancements in chips (like AWS Inferentia, NVIDIA IGX, etc.).

3. Synthetic data in radiology AI training

High-quality, diverse medical image data is scarce and sensitive. Now, generative AI is being used to create synthetic radiology data for safer, scalable model training.

‍

4. Integration with digital health ecosystems (FHIR, HL7)

AI in radiology is moving beyond isolated tools. Seamless integration with hospital-wide EHRs and FHIR-based APIs is key to delivering AI insights within existing clinical workflows.

5. AI agents for radiology case review

Multi-agent LLM systems (e.g., “Radiology Agent + Oncology Agent”) are being tested to collaborate across specialties and offer suggestions for diagnosis, treatment, and escalation.

6. AI + blockchain for imaging audit trails

As calls for AI explainability and accountability grow, blockchain technology is an option to immutably track every AI decision on a scan, what it flagged, why, and when.

7. AI-powered radiology education and simulation

AI is being used to train new radiologists through simulated diagnostic environments, offering interactive feedback and diverse case exposure.

What is the future for AI in radiology?

The next 5 years will redefine how radiology departments function and it couldn’t be more exciting.  

“At GoML, we are focused on helping hospitals build and adopt AI solutions that boost diagnostic speed and accuracy,” remarks Prashanna Rao, Head of Engineering, GoML.

1. Multimodal AI

Combining imaging data with clinical notes, lab reports, and genomics to offer a more holistic view of the patient’s condition.

2. Real-time AI at the point of care

Portable ultrasound devices with embedded AI will allow frontline physicians to make quick bedside assessments without waiting for radiology reports.

3. Federated learning

AI models can be trained across institutions without moving patient data, ensuring privacy while enabling collaboration.

4. Explainable AI

New tools are emerging to help radiologists understand why AI flagged a particular lesion, building confidence and enabling learning.

5. AI as a second reader

In situations like mammography or lung cancer screening, AI will act as a second reader, reducing the need for double reads while improving detection rates.

Addressing common concerns around AI in radiology

1. Will AI replace radiologists?

No. AI is a tool, not a substitute. While AI can detect patterns, it lacks clinical context, patient history, and nuanced judgment that only trained radiologists can offer. The best outcomes come from human-AI collaboration, not replacement.

2. Is AI accurate enough for clinical use?

Yes, in specific use cases. Many AI tools are FDA-approved and have undergone clinical validation. However, AI models must be tested on local populations and integrated responsibly with existing workflows.

3. What about bias in medical imaging AI?

Bias can creep in if training data isn’t diverse. For example, an AI trained only on European populations may underperform on scans from African or Asian patients. Mitigating bias requires diverse datasets, ongoing evaluation, and transparency.

Artificial intelligence isn’t here to take over radiology, it’s here to amplify human expertise.

From accelerating workflows to spotting the invisible, AI holds the potential to make radiology more accurate, efficient, and accessible. But successful adoption will depend on thoughtful integration, robust validation, and keeping the human-in-the-loop.

For radiologists, the future isn’t about competing with AI, but learning how to work with it, because together, they can deliver better patient outcomes at scale.

Accelerate diagnostic accuracy and reduce doctor workloads with AI solutions. Trust GoML to help you navigate the future of radiology with confidence.