Artificial Intelligence and Advanced Melanoma: Treatment Management Implications.

Journal: Cells
Published Date:

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.

Authors

  • Antonino Guerrisi
    Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy.
  • Italia Falcone
    SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.
  • Fabio Valenti
    UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.
  • Marco Rao
    Enea-FSN-TECFIS-APAM, C.R. Frascati, via Enrico Fermi, 45, 00146 Rome, Italy.
  • Enzo Gallo
    Pathology Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.
  • Sara Ungania
    Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena Institute, 00144 Rome, Italy.
  • Maria Teresa Maccallini
    Departement of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy.
  • Maurizio Fanciulli
    SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy.
  • Pasquale Frascione
    Oncologic and Preventative Dermatology, IFO-San Gallicano Dermatological Institute-IRCCS, 00144 Rome, Italy.
  • Aldo Morrone
    Scientific Direction, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy.
  • Mauro Caterino
    Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy.