Artificial Intelligence in Medicine and Radiation Oncology

Vincent Weidlich, Georg A. Weidlich
<span title="2018-04-13">2018</span> <i title="Cureus, Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/74mctp3frjfttdv6dx4dzh6lbm" style="color: black;">Cureus</a> </i> &nbsp;
Artifical Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the
more &raquo; ... efficiency and accuracy of radiation oncology operations.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7759/cureus.2475">doi:10.7759/cureus.2475</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29904616">pmid:29904616</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5999390/">pmcid:PMC5999390</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/apqe5uad4bc3fhpudvrptxstga">fatcat:apqe5uad4bc3fhpudvrptxstga</a> </span>
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