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How Artificial Intelligence is used in Oncology Practice

Artificial intelligence (AI) is a computer science that develops software programs and other entities that perform tasks intelligently or help to make decisions. It has been developed and used in healthcare for a long time to automate clinical tools such as the automated interpretation of electrocardiograms.

Today, artificial intelligence is harnessed in oncology to help solve ‘big’ problems, such as processing and interpreting large amounts of clinical and research data. This includes reading imaging or results from broad genetic testing panels. The use of artificial intelligence in oncology is minimal, but still being studied in various areas.

Artificial Intelligence in Cancer Screening and Diagnosis

The US Food and Drug Administration has approved several AI platforms to assist in evaluating medical imaging, including identifying lesions that are suspected to be cancer. Some other platforms help in visualizing and manipulating images from computed tomography or magnetic resonance imaging (MRI) and flag suspicious tumor locations.

There are several artificial intelligence platforms in oncology used to evaluate mammography images and even help in diagnosing breast abnormalities. Others are used to analyze lung nodules in patients undergoing screening for lung cancer.

AI is currently being studied in various areas of cancer diagnosis and screening. In dermatology, for example, skin lesions are biopsied based on the assessment of the lesion appearance by the dermatologist or primary care provider. Studies are trying to evaluate its use in supplementing or replacing the work of clinicians to make the overall process more efficient.

With the advancement in technology, health institutions can now create vast amounts of data. But the challenge is their inability to assess the large chunks of data and point out meaningful patterns. Artificial intelligence is currently being developed and used extensively to mine the data to unearth important findings, process, and condense the information represented by the data and also find meaningful patterns.

Tools like AI can be useful in a research setting to help scientists look for novel targets for new cancer therapies. It can also help to further their understanding of underlying cancer processes. In clinical settings, artificial intelligence can help generate and process patients’ health records and also support clinicians to make decisions that will help improve the quality of care.

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