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NEW AI PAPER: "Machine learning applications in gynecological cancer: A critical review"

"Machine learning applications in gynecological cancer: A critical review"


This brand new paper, being a collaborative work of the Medical School, Natl. and Kapod. University of Athens (NKUA) and the School of Electrical and Computer Engineering, Natl. Techn. University of Athens (NTUA), provides an in depth critical review of artificial intelligence (machine learning) models for the personalization and optimization of the overall handling of gynecological cancer, including several gynecological cancer entities. Diagnosis, prognosis, treatment plan and overall survival are examples of the aspects addressed. Current technical and ethical concerns, regarding the future clinical implementation of such models, are also addressed.


The paper has been co-authored by:


Oraianthi Fiste (NKUA, a), Michalis Liontos (NKUA, a), Flora Zagouria (NKUA, a), Georgios S. Stamatakos (NTUA, b), Meletios Athanasios Dimopoulos (NKUA, a)


(a) Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece


(b) In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece (https://lnkd.in/eNxXb7V).


The paper has been published in Critical Reviews in Oncology/Hematology, Volume 179, November 2022, 103808


IMPORTANT NOTE!


A personalized URL providing 50 days' free access to the article has been created. Anyone clicking on this link before October 29, 2022 will be taken directly to the final version of your article on ScienceDirect, which they are welcome to read or download. No sign up, registration or fees are required.


The link is the following:https://www.sciencedirect.com/science/article/pii/S1040842822002323?dgcid=coauthor