Artificial Intelligence in Radiology: Transforming Cancer Detection and Diagnosis

Area of Science:

• Radiological Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is becoming essential in radiological oncology for image interpretation, tumor analysis, and clinical decisions.
• AI applications are rapidly advancing across major cancer types, including breast, lung, prostate, brain, gastrointestinal, and metastatic diseases.

Purpose of the Study:

• To review state-of-the-art AI developments in radiological oncology.
• To explore AI integration with various imaging modalities and its impact on workflow.
• To identify challenges and propose strategies for effective AI implementation in clinical practice.

Main Methods:

• Focus on deep learning, radiomics, and radiogenomics frameworks.
• Analysis of AI applications across diverse cancer types and imaging modalities (CT, MRI, PET/CT, mammography).
• Examination of AI's impact on workflow, reporting, and radiologist efficiency.

Main Results:

• AI demonstrates significant improvements in lesion detection, segmentation, risk prediction, and molecular phenotype inference.
• AI performance metrics often match or exceed those of experienced radiologists.
• AI integration impacts workflow triage, report standardization, and radiologist efficiency.

Conclusions:

• Key challenges include model generalizability, data silos, regulatory issues, and the need for explainable AI.
• Strategies like federated learning and data harmonization are crucial for effective implementation.
• AI is positioned as a collaborative partner for data-driven, personalized oncologic imaging within precision medicine.