The Contribution of AI in Cancer Care

The Contribution of AI in Cancer Care

Several AI/ML models for breast cancer prognosis have successfully moved to clinical use. These models help to accurately determine the risk of a patient suffering from a relapse on the basis of which treatment can be personalized.

Fremont, CA: The incidence of cancer continues to increase worldwide. According to the latest data from the WHO cancer database GLOBOCAN, 19.3 million new cases of cancer were reported in 2020. This figure is expected to rise to 27.5 million new cases of cancer diagnosed each year by 2040. Thus, cancer will remain a key global health issue and will make use of a significant portion of our healthcare resources.

By applying AI and machine learning to multiple data sources—omics data, electronic health records, sensor/usable data, environmental and lifestyle data—researchers are taking the first steps towards developing personalised treatments for diseases from cancer to depression. AI is in action today and is making great strides in cancer treatment by leveraging the patient's medical history and tumour characteristics to help generate multiple treatment options.

Several AI/ML models for breast cancer prognosis have successfully moved to clinical use. These models help to accurately determine the risk of a patient suffering from a relapse on the basis of which treatment can be personalised. If a patient with breast cancer has a low risk of relapse, chemotherapy and all its side effects could potentially be avoided. Other localised cancer treatments, such as radiation, also increasingly rely on AI. Radiation oncologists are already using AI-driven software to develop plans for personalised radiation therapy.

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Other localised cancer treatments, such as radiation, also increasingly rely on AI. Radiation oncologists are already using AI-driven software to develop plans for personalised radiation therapy.

AL/ML may be used in multiple phases of new drug discovery, including the design of the chemical/protein structure of drugs, target validation, drug safety investigation and management of clinical trials. It is hoped that the use of AI/ML in drug discovery will not only help significantly reduce the cost of bringing new drugs onto the market, but will also make the drug discovery process faster (currently 10-15 years, including clinical trials) and more cost-effective (currently costing almost $1 billion per new drug). Companies today use deep learning software to sift through millions of possible molecules in a day or two, which would normally take months through traditional methods.