Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging

• Published in: Cancers Vol. 17; no. 9; p. 1510
• Main Authors: Pallumeera, Mustaqueem, Giang, Jonathan C., Singh, Ramanpreet, Pracha, Nooruddin S., Makary, Mina S.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.04.2025; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Biopsy; Breast cancer; Cancer; Care and treatment; Classification; Deep learning; Diagnosis; Health aspects; Health care; Learning algorithms; Machine learning; Magnetic resonance imaging; Mammography; Medical imaging; Medical screening; Methods; Oncology; Patients; Precision medicine; Privacy, Right of; Prostate; Radiomics; Review; Risk assessment; Segmentation; Surveillance; Tomography; Workloads; United States; cancer; imaging; radiology; artificial intelligence
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers17091510

Artificial intelligence (AI) is revolutionizing cancer imaging, enhancing screening, diagnosis, and treatment options for clinicians. AI-driven applications, particularly deep learning and machine learning, excel in risk assessment, tumor detection, classification, and predictive treatment prognosis. Machine learning algorithms, especially deep learning frameworks, improve lesion characterization and automated segmentation, leading to enhanced radiomic feature extraction and delineation. Radiomics, which quantifies imaging features, offers personalized treatment response predictions across various imaging modalities. AI models also facilitate technological improvements in non-diagnostic tasks, such as image optimization and automated medical reporting. Despite advancements, challenges persist in integrating AI into healthcare, tracking accurate data, and ensuring patient privacy. Validation through clinician input and multi-institutional studies is essential for patient safety and model generalizability. This requires support from radiologists worldwide and consideration of complex regulatory processes. Future directions include elaborating on existing optimizations, integrating advanced AI techniques, improving patient-centric medicine, and expanding healthcare accessibility. AI can enhance cancer imaging, optimizing precision medicine and improving patient outcomes. Ongoing multidisciplinary collaboration between radiologists, oncologists, software developers, and regulatory bodies is crucial for AI’s growing role in clinical oncology. This review aims to provide an overview of the applications of AI in oncologic imaging while also discussing their limitations.