Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?
artificial intelligence
machine learning
thyroid cancer
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
10 Jul 2022
10 Jul 2022
Historique:
received:
09
05
2022
revised:
24
06
2022
accepted:
08
07
2022
entrez:
27
7
2022
pubmed:
28
7
2022
medline:
28
7
2022
Statut:
epublish
Résumé
Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.
Identifiants
pubmed: 35884418
pii: cancers14143357
doi: 10.3390/cancers14143357
pmc: PMC9315681
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
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