The introduction of artificial intelligence dates back to 1950, when Alan Turing and John McCarthy pioneered the field. Originally, Turing invented the idea of using computers at that time to mimic intelligence and critical thinking, while McCarthy coined the term, “artificial intelligence.” Artificial intelligence developed from the “if” and “then” rules to the vastly intelligent complex algorithms that rule today's world.
In oncology (the study of cancer), artificial intelligence has been extremely transformative. AI applications in cancer encompass various different aspects such as cancer detection, screening, diagnosis, genomics, characterization, and treatment strategy formulation. AI has been extremely helpful in integrating multi-omics data with advanced and high-performing computing and deep-learning strategies.
Recent Innovations and Applications in Cancer Research
Cancer Diagnostics
Artificial intelligence’s use in oncology has proven to be incredibly useful, particularly in diagnosing patients using radiology and pathology. Artificial intelligence can help in interpreting Magnetic Resonance Imagery (MRI) and Computed Tomography (CT) scans. This makes these interpretations more accurate and efficient. These AI tools have made a significant impact by improving management and outcomes in cancer care.
Precision Oncology
AI precision in oncology focuses on tailoring treatments based on the tumor’s molecular profile. AI integrates radiogenomics which combines radiomics and genomic analysis.
The use of computational tools can explore cancer biology and predict treatment responses. The approach for integrating diverse data sets a path for personalized medicine, optimizing clinical outcomes and reducing healthcare costs.
Radiomics and Machine Learning Techniques
Radiomics involves analyzing medical images to identify patterns that are not perceivable to human eyes. These patterns from the AI algorithms assist in many clinical decision-making processes. AI techniques used in cancer imaging may include a variety of different models, from simple regression models to more complex Convolutional Neural Networks (CNNs).
Sources:
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Jaber, Nadia. "Can Artificial Intelligence Help See Cancer in New, and Better, Ways?" National Cancer Institute, United States Government, 22 Mar. 2023, www.cancer.gov/news-events/cancer-currents-blog/2022/artificial-intelligence-cancer-imaging.
"Radiomics with artificial intelligence: a practical guide for beginners." PubMed Central, edited by Burak Kocak, Emine S. Durmaz, Ece Ates, and Ozgur Kilichesmez, United States Government, 4 Sept. 2019, www.ncbi.nlm.nih.gov/pmc/articles/PMC6837295/.
"Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations." ASCO Educational Book, edited by Jacob T. Shreve, Sadia A. Khanani, and Tufia C. Haddad, ascopubs.org/doi/full/10.1200/EDBK_350652.
