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Artificial Intelligence and Advanced Melanoma: Treatment Management Implications.

Authors :
Guerrisi, Antonino
Falcone, Italia
Valenti, Fabio
Rao, Marco
Gallo, Enzo
Ungania, Sara
Maccallini, Maria Teresa
Fanciulli, Maurizio
Frascione, Pasquale
Morrone, Aldo
Caterino, Mauro
Source :
Cells (2073-4409); Dec2022, Vol. 11 Issue 24, p3965, 13p
Publication Year :
2022

Abstract

Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20734409
Volume :
11
Issue :
24
Database :
Complementary Index
Journal :
Cells (2073-4409)
Publication Type :
Academic Journal
Accession number :
160958370
Full Text :
https://doi.org/10.3390/cells11243965