Artificial intelligence technology in oncology: a new technological paradigm [article]

Mario Coccia
<span title="2019-05-14">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Artificial Intelligence (AI) technology is based on theory and development of computer systems able to perform tasks that normally require human intelligence. In this context, deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Application of these methods to medical imaging can assist pathologists in the detection of cancer subtype, gene mutations and/or metastases for
more &raquo; ... ng appropriate therapies. The purpose of this study is to show the emerging application of AI in medical imaging to detect lung and breast cancer. Moreover, this study shows the comparative evolutionary pathways of this emerging technology for three critical cancers: lung, breast and thyroid. A main finding of this study is the recognition that, since the late 1990, the sharp increase of technological trajectories of AI technology applied in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to find new techniques for a more accurate detection, characterization and monitoring as well as to apply efficiently anticancer therapies that increase the progression-free survival of patients: the so-called mortality-driven AI technological trajectories. Results also suggest that this new technology can generate a technological paradigm shift for diagnostic assessment of any cancer type. However, application of these methods to medical imaging requires further assessment and validation to assist pathologists to increase the efficiency of their workflow in both routine tasks and critical cases of diagnostics.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.06871v1">arXiv:1905.06871v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wtzcracchneghlon4t4vuqqkhu">fatcat:wtzcracchneghlon4t4vuqqkhu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823155356/https://arxiv.org/ftp/arxiv/papers/1905/1905.06871.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b2/ad/b2ad1aa2fc6adb0b26250782bf1cc18324be2b41.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.06871v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>