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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