Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review.

Introduction: There is a growing interest in leveraging artificial intelligence (AI) technologies to enhance various aspects of clinical trials. The goal of this systematic review is to assess the impact of implementing AI approaches on different aspects of oncology clinical trials.

Methods: Pertinent keywords were used to find relevant articles published in PubMed, Scopus, and Google Scholar databases, which described the clinical application of AI approaches.

Impact of Recent Translational and Therapeutic Developments on Clinical Course of BCR::ABL1-Positive and -Negative Myeloproliferative Neoplasms.

Despite the study of BCR::ABL1-positive and -negative myeloproliferative neoplasms (MPNs) providing seminal insights into cancer biology, tumor evolution and precision oncology over the past half century, significant challenges remain. MPNs are clonal hematopoietic stem cell-derived neoplasms with heterogenous clinical phenotypes and a clonal architecture which impacts the often-complex underlying genetics and microenvironment. The major driving molecular abnormalities have been well characterized, but debate on their role as disease-initiating molecular lesions continues.

PARADIGM-PV: a randomized, multicenter phase 4 study to assess the efficacy and safety of ropeginterferon alfa-2b in patients with low- or high-risk polycythemia vera.

Polycythemia vera (PV) is characterized by clonal hematopoietic stem or progenitor cells with constitutively active somatic mutation(s) in the Janus kinase 2 gene. Phlebotomy (Phl) and aspirin are often used alone for low-risk PV patients. However, data from the Low-PV study demonstrated that Phl and aspirin may not be adequate for patients.

Addressing catastrophic forgetting in payload parameter identification using incremental ensemble learning.

Collaborative robots (cobots) are increasingly integrated into Industry 4.0 dynamic manufacturing environments that require frequent system reconfiguration due to changes in cobot paths and payloads. This necessitates fast methods for identifying payload inertial parameters to compensate the cobot controller and ensure precise and safe operation.