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The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey.

Authors :
Giannitto, Caterina
Carnicelli, Giorgia
Lusi, Stefano
Ammirabile, Angela
Casiraghi, Elena
De Virgilio, Armando
Esposito, Andrea Alessandro
Farina, Davide
Ferreli, Fabio
Franzese, Ciro
Frigerio, Gian Marco
Lo Casto, Antonio
Malvezzi, Luca
Lorini, Luigi
Othman, Ahmed E.
Preda, Lorenzo
Scorsetti, Marta
Bossi, Paolo
Mercante, Giuseppe
Spriano, Giuseppe
Source :
Journal of Personalized Medicine; Apr2024, Vol. 14 Issue 4, p341, 11p
Publication Year :
2024

Abstract

Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754426
Volume :
14
Issue :
4
Database :
Complementary Index
Journal :
Journal of Personalized Medicine
Publication Type :
Academic Journal
Accession number :
176874934
Full Text :
https://doi.org/10.3390/jpm14040341