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There is a rising interest in the use cases of AI in oncology to enhance disease diagnosis, management, and the development of effective treatments.
FREMONT, CA: Artificial intelligence (AI) has captured the world's imagination and created enthusiasm for its potential to improve lives. Presently, AI plays an integral role in the daily routines and the connections with media, transportation, and communications. There is an interest in AI applications in healthcare to enhance disease diagnosis, management, and the development of effective treatments. Given the large number of patients diagnosed with cancer and the significant amount of data generated during cancer treatment, there is a specific interest in applying AI to improve oncologic care. In oncology, AI is not being used widely yet, but its use is studied in many areas.
There are many AI platforms approved by the FDA to help evaluate medical imaging, including for finding suspicious lesions that may be cancer. Some platforms assist in visualizing and manipulate images from magnetic resonance imaging or computed tomography and flag areas. For example, there are many AI platforms for assessing mammography images and, in some cases, assist in diagnosing breast abnormalities. There is also an AI platform supporting analyzing lung nodules in individuals who are being screened for lung cancer. AI is also being studied in areas of cancer screening and diagnosis. In dermatology, skin lesions are biopsied on a dermatologist's or care provider's evaluation of the lesion's appearance. Studies are evaluating the usage of AI to either supplement or replace the clinician's work, with the ultimate aim of making the process more efficient.
As technology has enhanced, the world now has the potential to create a vast amount of data. This highlights an issue — individuals have limited potentials to evaluate large chunks of data and find meaningful patterns. AI is being designed and used to help mine these data for essential findings, process and condense the information represented, and look for meaningful patterns. Such tools will be useful in the research setting as researchers look for new targets for novel anticancer therapies or extend their understanding of underlying disease processes. AI would be useful in the clinical setting, specifically that electronic health records are being leveraged and real-world data are being created from patients.