Claudia Giorgio
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Claudia Giorgio has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Antonio Pinto
Department of Radiology, CTO Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
1 shared publication - Carmine Frasca
Department of Radiology, CTO Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
1 shared publication - Marco De Giorgi
Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
1 shared publication - Renato Cuocolo
Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
1 shared publication - Teresa Perillo
Department of Radiology, CTO Hospital, Azienda Ospedaliera dei Colli, Naples, Italy
1 shared publication
Latest Publications
- Open Access
The Role of Machine Learning in the Most Common Hematological Malignancies: A Narrative Review
Background: Hematologic malignancies are a group of heterogeneous neoplasms which originate from hematopoietic cells. The most common among them are leukemia, lymphoma, and multiple myeloma. Machine learning (ML) is a subfield of artificial intelligence that enables the analysis of large amounts of data, possibly finding hidden patterns. Methods: We performed a narrative review about recent applications of ML in the most common hematological malignancies. We focused on the most recent scientific literature about this topic. Results: ML tools have proved useful in the most common hematological malignancies, in particular to enhance diagnostic work-up and guide treatment. Conclusions: Although ML has multiple possible applications in this field, there are some issue that have to be fixed before they can be used in daily clinical practice.
Open AccessThe Role of Machine Learning in the Most Common Hematological Malignancies: A Narrative Review
Background: Hematologic malignancies are a group of heterogeneous neoplasms which originate from hematopoietic cells. The most common among them are leukemia, lymphoma, and multiple myeloma. Machine learning (ML) is a subfield of artificial intelligence that enables the analysis of large amounts of data, possibly finding hidden patterns. Methods: We performed a narrative review about recent applications of ML in the most common hematological malignancies. We focused on the most recent scientific literature about this topic. Results: ML tools have proved useful in the most common hematological malignancies, in particular to enhance diagnostic work-up and guide treatment. Conclusions: Although ML has multiple possible applications in this field, there are some issue that have to be fixed before they can be used in daily clinical practice.