Artificial intelligence (AI) has played a major role in recent developments in healthcare, particularly in cancer diagnosis. This review investigated the dynamic role of AI in the detection of cancer and provides insights into the fundamental contributions of AI in the revolutionization of cancer detection methodologies, focusing on the role of AI in radiology and medical imaging and highlighting AI's advancements in enhancing accuracy and efficiency in identifying cancerous lesions. Furthermore, it explained the indispensable role of pathology and histopathology in cancer diagnosis, emphasizing AI's potential to augment traditional methods and improve diagnostic precision. Genomics and personalized medicine were explored as integral components of cancer detection, illustrating how AI facilitates tailored treatment strategies by analyzing vast genomic datasets. Additionally, the discussion encompassed clinical decision support systems, explaining their utility in aiding healthcare professionals with evidence-based insights for more informed decision-making in cancer detection and management. Finally, the review addressed the challenges and future directions in the integration of AI into cancer detection practices, highlighting the need for continued research and development to overcome existing limitations and realize the full potential of AI-driven solutions in combating cancer.
Citation: Praveen Kumar, Sakshi V. Izankar, Induni N. Weerarathna, David Raymond, Prateek Verma. The evolving landscape: Role of artificial intelligence in cancer detection[J]. AIMS Bioengineering, 2024, 11(2): 147-172. doi: 10.3934/bioeng.2024009
Artificial intelligence (AI) has played a major role in recent developments in healthcare, particularly in cancer diagnosis. This review investigated the dynamic role of AI in the detection of cancer and provides insights into the fundamental contributions of AI in the revolutionization of cancer detection methodologies, focusing on the role of AI in radiology and medical imaging and highlighting AI's advancements in enhancing accuracy and efficiency in identifying cancerous lesions. Furthermore, it explained the indispensable role of pathology and histopathology in cancer diagnosis, emphasizing AI's potential to augment traditional methods and improve diagnostic precision. Genomics and personalized medicine were explored as integral components of cancer detection, illustrating how AI facilitates tailored treatment strategies by analyzing vast genomic datasets. Additionally, the discussion encompassed clinical decision support systems, explaining their utility in aiding healthcare professionals with evidence-based insights for more informed decision-making in cancer detection and management. Finally, the review addressed the challenges and future directions in the integration of AI into cancer detection practices, highlighting the need for continued research and development to overcome existing limitations and realize the full potential of AI-driven solutions in combating cancer.
Artificial intelligence
World Health Organization
Deep learning
Machine learning
Magnetic Resonance Imaging
Computed Tomography
K-nearest neighbors
Support Vector Machine
Convolutional neural network
Neural network
Generative adversarial network
Position Emission Tomography
Food and Drug Administration
Computer-assisted diagnosis
Full Field Digital Mammography
Advanced intelligent Clear IQ Engine
Filtered back projection
Single-photon emission computed tomography
Diffusion-weighted imaging
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