Original article was published on artificial intelligence
Artificial intelligence (AI) comprises a type of computer science that develops entities, such as software programs, that can intelligently perform tasks or make decisions.1 The development and use of AI in health care is not new; the first ideas that created the foundation of AI were documented in 1956, and automated clinical tools that were developed between the 1970s and 1990s are now in routine use. These tools, such as the automated interpretation of electrocardiograms, may seem simple, but are considered AI.
Today, AI is being harnessed to help with “big” problems in medicine — such as processing and interpreting large amounts of data in research and in clinical settings, including reading imaging or results from broad genetic-testing panels.1 In oncology, AI is not yet being used broadly, but its use is being studied in several areas.
Screening and Diagnosis
There are several AI platforms approved by the US Food and Drug Administration (FDA) to assist in the evaluation of medical imaging, including for identifying suspicious lesions that may be cancer.2 Some platforms help to visualize and manipulate images from magnetic resonance imaging (MRI) or computed tomography (CT) and flag suspicious areas. For example, there are several AI platforms for evaluating mammography images and, in some cases, help to diagnose breast abnormalities. There is also an AI platform that helps to analyze lung nodules in individuals who are being screened for lung cancer.1,3
AI is also being studied in other areas of cancer screening and diagnosis. In dermatology, skin lesions are biopsied based on a dermatologist’s or primary care provider’s assessment of the appearance of the lesion.1 Studies are evaluating the use of AI to either supplement or replace the work of the clinician, with the ultimate goal of making the overall process more efficient.
As technology has improved, we now have the ability to create a vast amount of data. This highlights a challenge — individuals have limited capabilities to assess large chunks of data and identify meaningful patterns. AI is being developed and used to help mine these data for important findings, process and condense the information the data represent, and look for meaningful patterns.
Such tools would be useful in the research setting, as scientists look for novel targets for new anticancer therapies or to further their understanding of underlying disease processes. AI would also be useful in the clinical setting, especially now that electronic health records are being used and real-world data are being generated from patients.