By Dr. Mustafa Elattar

Associate Professor of Biomedical Engineering | Director of AI Program, Nile University | CTO & VP, Ramyro

In today’s healthcare systems, data is the new stethoscope. Nowhere is this more evident than in radiology, a specialty rich with structured and unstructured data—from pixel-level medical images to clinical notes and dictated reports. But with such a vast sea of information, how do we extract timely, accurate insights to optimize care?

This is where Large Language Models (LLMs) are making practical differentiation.

From Pixels to Paragraphs: Why Radiologists Should Care About LLMs

Radiologists already work at the frontier of technology, using advanced imaging tools and AI models for detection and quantification. But with LLMs, we’re seeing the rise of AI systems that can:

• Draft radiology reports from image findings
• Translate clinical impressions into layman’s terms for patient communication
• Alert for missed follow-ups or critical incidental findings
• Support triage in the Emergency Room based on presenting symptoms and imaging data
• Automate documentation and reduce cognitive load

This transforms LLMs into clinical co-pilots—augmenting rather than replacing human radiologists.

Clinical Decision Support (CDSS) Reimagined with LLMs

Traditional CDSS systems use structured data to guide care. LLMs extend this by unlocking the 80% of unstructured data free-text reports, image annotations, and radiologist impressions—that have been historically underutilized.

Imagine this:

• A chest X-ray report contains subtle clues about cardiomegaly and interstitial markings figure 1.
• An LLM, integrated into your PACS, identifies a high-risk signature and prompts a recommendation: “Consider cardiac evaluation. Would you like to flag this case for cardiology consult?”

Figure 1 A Chest Xray with a justification of cardiomegaly (Source: Roboflow)

This is not science fiction. It’s prompt engineering at work, in real time.

Prompt Engineering in Radiology: The New Skillset?

Yes, radiologists may soon need to understand prompt engineering—how to craft the right query to get the most accurate and explainable AI output.

Some examples:

• Chain-of-Thought Prompting: “Patient with bilateral ground-glass opacities—think step-by-step: what’s the likely diagnosis, differential, and follow-up imaging?”
• Role Prompting: “You are a radiologist preparing a report for a 70-year-old with hip pain. Please generate the impression.”
• Instruction-Based Prompting: “List top 3 findings on this CT brain, along with urgency levels.”

This ensures clarity, structure, and safety in clinical output.

Challenges Ahead: Bias, Hallucinations, and Explainability

While promising, LLMs are not without risk. Radiology, being a high-stakes field, demands:

• High accuracy
• Explainable outputs
• Ethical data usage
• Human-in-the-loop validation

Techniques like Retrieval-Augmented Generation (RAG) and specialty-specific fine-tuning will be key in overcoming these hurdles.

Where Do We Go From Here?

The future is about convergence:

• Multimodal AI combining text and image data
• Radiology copilot systems integrated with RIS/PACS
• Open-source LLMs like Mistral, LLaMA, and Falcon enabling local deployment for data-sensitive environments
• Explainable AI dashboards that highlight how conclusions were derived

This is our moment to shape how radiology evolves in the AI era.

#Radiology #AIinHealthcare #LLMs #ClinicalDecisionSupport #PromptEngineering #MedicalAI #RadiologistCopilot #DigitalHealth #Ramyro

A Radiologist’s Perspective – Dr. Thanaa Mohannad, CMO, RAMYRO Inc.,specializing in integrating AI solution into Radiological practices to improve diagnostic precision and patient care

Introduction

Lung cancer remains one of the leading causes of cancer-related deaths globally, with over 2.5 million new cases and 1.8 million deaths recorded in 2022 alone, according to the World Health Organization (WHO). Early-stage detection is strongly correlated with better prognosis and survival rates.

However, traditional screening methods are challenged by limitations in radiologist availability, interpretive variability, and diagnostic sensitivity. Enter artificial intelligence (AI) — a transformative ally in reshaping CT-based lung screening programs for both clinical practice and national-level deployments.

The Case for AI in Lung Cancer Screening AI in radiology is no longer theoretical. Numerous studies have validated the performance of AI-powered tools in enhancing diagnostic accuracy and workflow efficiency, especially in low-dose chest CT for lung cancer screening. These tools employ deep learning, primarily convolutional neural networks (CNNs), to detect and classify pulmonary nodules with high precision.

A study published in Nature Medicine showed that a developed deep learning model outperformed six radiologists in lung cancer prediction on CT scans. Similarly, AI models dataset have demonstrated sensitivity rates exceeding 90% for nodule detection.

Scientific Foundations and Technologies

1. Convolutional Neural Networks (CNNs):

These are the backbone of image-based AI systems. In lung screening, 3D CNN architectures extract spatial features from CT volumes, enabling robust nodule detection and malignancy scoring.

2. Radiomics and Feature Extraction: Whats a (multi-omics)?

AI algorithms analyze texture, shape, and intensity-based radiomic features that may elude human eyes. Integration of these features with clinical and genomic data (multi-omics) can significantly improve early diagnosis and risk stratification.

3. Explainable AI (XAI)

Radiologists are increasingly adopting XAI systems that provide visual saliency maps, decision trees, or concept-based reasoning, enhancing transparency and clinician trust in AI outputs.

From Screening to Actionable Insights

AI tools are not only detecting nodules but also characterizing them—assessing growth rates, calcification patterns, and spiculated edges, which are indicative of malignancy. When paired with AI-enabled risk prediction models, these insights allow for better triaging and personalized follow-up protocols.

Virtual Lung Screening Trials (VLST) are using simulated data to test AI models before clinical deployment, enhancing safety and cost-effectiveness.

Integrating AI into Radiology Workflows

From a practical perspective, AI can:

• Triage Normal Scans: AI identifies normal scans with high negative predictive value, reducing workload for radiologists.
• Act as a Second Reader: AI augments junior radiologists’ performance, increasing sensitivity from 40% to over 90%, as shown in COVID-19 CT screening trials.
• Enable Prioritization: Tools can prioritize critical scans, shortening time to diagnosis.

Challenges and Considerations

• Data Bias and Generalizability: AI models must be trained on diverse datasets to avoid racial, age, and gender biases.
• Regulatory and Ethical Oversight: Explainability, patient consent, and auditability must be ensured before widespread adoption.
• Clinical Validation: Prospective, randomized trials are essential to establish real-world efficacy and safety.

AI-driven CT lung cancer screening represents a paradigm shift in radiological practice. Whether implemented in hospital settings or as part of national public health initiatives, AI tools can bridge the gap between early detection and timely intervention, ultimately saving lives.

As radiologists, our role is to integrate these technologies responsibly, ensuring they complement our clinical expertise while expanding access to life-saving diagnostics.

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#AIinRadiology #RAMYRO

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The Digital Imaging and Communications in Medicine (DICOM) standard, now at version 3.0, is the backbone of modern medical imaging. Unlike standard image formats like JPEG or PNG, DICOM is not just about images—it’s about medical information. It provides a comprehensive framework for storing, transmitting, and managing imaging data in healthcare environments, from acquisition devices to PACS servers and AI systems.

1. DICOM vs. JPEG/PNG: What’s the Difference?

JPEG/PNG are general-purpose image formats used for display or web usage. They store image pixels but lack clinical context.

DICOM images, however:

• Contain rich metadata: Patient info, acquisition parameters, device details.
• Follow a structured hierarchy: Patient → Study → Series → Image.
• Support medical-specific compressions (lossless JPEG, JPEG 2000, RLE).
• Carry diagnostic information, including windowing, scaling, and orientation.
• Enable integration with hospital systems like PACS, RIS, and EMR.

2. DICOM File Structure: Key Concepts

🔹 Transfer Syntax

Defines how data is encoded (endian-ness, compression). Examples:

• Implicit VR Little Endian (default)
• JPEG Lossless (for image compression)
• Explicit VR Big Endian

🔹 Groups and Tags

DICOM files are composed of Data Elements identified by unique (Group, Element) tags. Example:

• (0010,0010) – Patient Name
• (0028,0010) – Rows
• (7FE0,0010) – Pixel Data

🔹 VR (Value Representation)

Specifies the data type for each tag (e.g., PN = Person Name, DA = Date, UI = Unique Identifier).

🔹 Compression Techniques

DICOM supports:

• JPEG Lossless / Lossy
• JPEG 2000
• RLE (Run-Length Encoding)
• MPEG2/MPEG4 for video loops

🔹 Still Image, Sequence, and Loops

• Still: Single-frame (e.g., X-ray)
• Sequence: Multi-frame (e.g., CT slices, MR series)
• Loop: Cine images or ultrasound clips

🔹 Window Leveling Techniques

Enhance contrast for diagnostic viewing.

• Window Width/Level (WW/WL): Linear contrast control
• Window LUT: Lookup tables for pixel value mapping
• Non-linear Windows: Sigmoid or logarithmic mappings for specific use cases

🔹 DICOM Overlay

Separate graphic layer (bitmap) for annotations, measurements, or AI markings. Stored in tags like (6000,3000) and rendered over the image.

🔹 Real-World Coordinates (3D Mapping)

Image position and orientation tags map pixels to real-world 3D coordinates (X, Y, Z):

• (0020,0032) – Image Position (Patient)
• (0020,0037) – Image Orientation (Patient) Essential for 3D reconstructions and navigation.

🔹 Information Object Definitions (IODs)

Each modality has a defined IOD. Examples:

• CR/DR – X-ray IOD
• CT – CT Image Storage IOD
• MR – MR Image Storage IOD
• MG – Mammography IOD
• US – Ultrasound Multi-frame IOD

🔹 Example: DICOM File Structure (CT Image)

3. DICOM Communication Protocol: SCU/SCP and Services

🔹 SCU vs. SCP

• SCU (Service Class User): Initiates communication (e.g., modality sending image).
• SCP (Service Class Provider): Responds to requests (e.g., PACS receiving image).

🔹 Association & Feedback

A DICOM Association is a network session where SCU and SCP negotiate supported services and transfer syntaxes. If an operation fails, a feedback/status code is returned.

🔹 C-STORE Service

Used to send/receive images.

• SCU: Sends image data to PACS.
• SCP: Receives and stores image data.

Example: CT modality (SCU) sends image to PACS (SCP) via C-STORE.

🔹 C-FIND / C-MOVE / C-GET: Query/Retrieve (Q/R)

• C-FIND (SCU): Queries PACS (SCP) for patient/study info.
• C-MOVE: Asks PACS to send images to another node.
• C-GET: Requests images directly within the session.

Workflow: A viewer queries PACS for a patient study (C-FIND), then retrieves images via C-MOVE.

🔹 Modality Worklist (MWL) and MPPS

• MWL: Allows a modality (e.g., US machine) to pull scheduled exams from RIS.
  • Reduces manual entry, ensures consistency.
• MPPS (Modality Performed Procedure Step): Sends status updates (started, completed) back to RIS.

Use Case: RIS schedules a CT scan → CT pulls data (MWL) → Sends back exam status (MPPS).

🔹 DICOM Print Service

• Sends image data to DICOM printers using Film Session, Film Box, and Image Box objects.
• Supports Grayscale or Color Image Boxes depending on image type.

Example: Mammogram images are printed in grayscale on a DICOM printer.

4. DICOM in Real-World Integrations

🔹 DICOMweb (WADO-RS, STOW-RS, QIDO-RS)

RESTful web services for:

• WADO-RS: Retrieve DICOM objects/images
• STOW-RS: Store DICOM objects via HTTP
• QIDO-RS: Query for studies/series/images

Enables browser-based PACS and AI systems to integrate without traditional DICOM DIMSE protocols.

🔹 DICOM Segmentation (SEG) and AI

• Encodes AI outputs as structured segmentations.
• Enables standardized labeling, overlay, and interoperability with PACS and viewers.

AI lung nodule detection creates DICOM SEG objects viewable in radiology workstations.

🔹 DICOM SR, Encapsulated PDF & MP4

• Structured Report (SR): Codified, searchable clinical content.
• Encapsulated PDF: Embeds documents (e.g., consent forms, lab results).
• Encapsulated MP4: Stores video clips from modalities or scopes.

Use Case: AI-generated reports stored as SR and integrated with the hospital EMR.

Thank You!

DICOM 3.0 is a vast, powerful standard critical to modern imaging and healthcare interoperability. If you’d like a deeper dive into any of the above topics, feel free to reach out—I’m happy to help!