RT journal article
T1 Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis
A1 Morán Sánchez, Julia
A1 Santiesteban Espejo, Antonio
A1 Martin-Piedra, Miguel Ángel
A1 Pérez-Requena, José
A1 García-Rojo, Marcial
A2 Medicina
K1 artificial intelligence
K1 hematopathology
K1 lymphoid neoplasms
K1 digital image analysis
K1 machine learning
AB Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People's Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.
PB MDPI
SN 2218-273X
YR 2021
FD 2021-06
LK http://hdl.handle.net/10498/25430
UL http://hdl.handle.net/10498/25430
LA eng
NO This research was funded by a postdoctoral grant (RH-0145-2020) from the Andalusia Health System, and with the EU FEDER ITI Grant for Cadiz Province PI-0032-2017.
DS Repositorio Institucional de la Universidad de Cádiz
RD 13-nov-2026