biomolecules Article Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis Julia Moran-Sanchez 1,2, Antonio Santisteban-Espejo 3,4,*, Miguel Angel Martin-Piedra 5, Jose Perez-Requena 3 and Marcial Garcia-Rojo 3,4 1 Division of Hematology and Hemotherapy, Puerta del Mar Hospital, 11009 Cadiz, Spain;
[email protected] 2 Ph.D Program of Clinical Medicine and Surgery, University of Cadiz, 11009 Cadiz, Spain 3 Pathology Department, Puerta del Mar Hospital, 11009 Cadiz, Spain;
[email protected] (J.P.-R.);
[email protected] (M.G.-R.) 4 Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), University of Cadiz, 11009 Cadiz, Spain 5 Tissue Engineering Group, Department of Histology, University of Granada, 18016 Granada, Spain;
[email protected] * Correspondence:
[email protected]; Tel.: +34-603470838 Abstract: 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 Citation: Moran-Sanchez, J.; integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we Santisteban-Espejo, A.; Martin-Piedra, identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata M.A.; Perez-Requena, J.; Garcia-Rojo, were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of M. Translational Applications of 525 documents were assessed by document type, research areas, source titles, organizations, and Artificial Intelligence and Machine countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis.