Artificial Intelligence to Facilitate Cancer Care

By Dr. Brenda Santellano – Research Associate at Georgia Cancer Center 

 

Dr. Finkelstein explored the transformative role of artificial intelligence (AI) in cancer care, outlining its applications across cancer care and discussing various AI methods and their impacts. 

 

AI is making significant advances from cancer screening and diagnosis to expediting drug discovery. Moreover, it enhances cancer surveillance, improves healthcare delivery, and even delves into mechanistic studies to resolve the complexities of the disease. 

 

Dr. Finkelstein also discussed the categories of AI, such as predictive and generative, along with concepts like supervised, semi-supervised, and unsupervised learning. 

 

Key studies underscore the tangible benefits of AI implementation. For instance, in mammography screening, AI-supported methodologies showcase comparable cancer detection rates to traditional double-reading, while significantly reducing workload. Similarly, AI-directed analytics during radiation therapy not only identify high-risk patients but also mitigate acute care visits, translating into substantial cost savings and improved patient experiences. 

 

Beyond diagnostics, AI extends its reach into pathology image analysis, drug response prediction, and the identification of social determinants of health. Initiatives like the Artificial Intelligence – Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) aim to bridge gaps and address disparities in healthcare delivery. 

 

Looking ahead, the future of AI in cancer care is promising. From personalized treatment regimens to enhanced patient monitoring and early detection, the potential applications are boundless. However, ethical considerations and regulatory frameworks remain paramount in this technological revolution, ensuring responsible and equitable AI deployment. 

 

 

References 

 

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2. Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ideker, T. (2020). Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell, 38(5), 672–684.e6. https://doi.org/10.1016/j.ccell.2020.09.014

 

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5. Clusmann, J., Kolbinger, F. R., Muti, H. S., et al. (2023). The future landscape of large language models in medicine. Communications Medicine, 3, 141. https://doi.org/10.1038/s43856-023-00370-1

 

6. Meskó, B., Topol, E. J. (2023). The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digital Medicine, 6(1), 120. https://doi.org/10.1038/s41746-023-00873-0

 

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No conflicts of interest