Artificial intelligence technology in oncology: a new technological\n paradigm

Abstract

Artificial Intelligence (AI) technology is based on theory and development of\ncomputer systems able to perform tasks that normally require human\nintelligence. In this context, deep learning is a family of computational\nmethods that allow an algorithm to program itself by learning from a large set\nof examples that demonstrate the desired behavior. Application of these methods\nto medical imaging can assist pathologists in the detection of cancer subtype,\ngene mutations and/or metastases for applying appropriate therapies. The\npurpose of this study is to show the emerging application of AI in medical\nimaging to detect lung and breast cancer. Moreover, this study shows the\ncomparative evolutionary pathways of this emerging technology for three\ncritical cancers: lung, breast and thyroid. A main finding of this study is the\nrecognition that, since the late 1990, the sharp increase of technological\ntrajectories of AI technology applied in cancer imaging seems to be driven by\nhigh rates of mortality of some types of cancer (e.g., lung and breast) in\norder to find new techniques for a more accurate detection, characterization\nand monitoring as well as to apply efficiently anticancer therapies that\nincrease the progression-free survival of patients: the so-called\nmortality-driven AI technological trajectories. Results also suggest that this\nnew technology can generate a technological paradigm shift for diagnostic\nassessment of any cancer type. However, application of these methods to medical\nimaging requires further assessment and validation to assist pathologists to\nincrease the efficiency of their workflow in both routine tasks and critical\ncases of diagnostics.\n

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