Artificial Intelligence-Assisted Drug and Biomarker Discovery for Glioblastoma: A Scoping Review of the Literature DOI Open Access
Luana Conte, Gerardo Caruso, Armelle Philip

и другие.

Cancers, Год журнала: 2025, Номер 17(4), С. 571 - 571

Опубликована: Фев. 7, 2025

Background: Artificial intelligence (AI) has emerged as a transformative tool in healthcare, particularly drug and biomarker discovery, where it can enhance precision, streamline discovery processes, optimize treatment strategies. Despite its potential, the application of AI glioblastoma (GB) research, especially identifying novel biomarkers therapeutic targets, remains underexplored. The aim this review is to map existing literature on AI-driven approaches for GB, highlighting key trends gaps current research. Design: Following PRISMA methodology, scoping examined studies published between 2012 2024. Searches were conducted across multiple databases, including MEDLINE (PubMed), Scopus, Cochrane Library, Web Science (WOS). Eligible screened, relevant data extracted synthesized provide comprehensive overview applications GB Results: A total 224 records identified, 210 from PubMed, 104 4 WOS, 6 Library. After screening applying eligibility criteria, 33 included final review. These showcased diverse methodologies applied both identification, focusing various aspects biology treatment. Conclusions: This reveals an increasing interest strategies with promising initial results. However, further large-scale, rigorous are needed validate real-world development standardized protocols reproducibility clinical translation.

Язык: Английский

Artificial Intelligence-Assisted Drug and Biomarker Discovery for Glioblastoma: A Scoping Review of the Literature DOI Open Access
Luana Conte, Gerardo Caruso, Armelle Philip

и другие.

Cancers, Год журнала: 2025, Номер 17(4), С. 571 - 571

Опубликована: Фев. 7, 2025

Background: Artificial intelligence (AI) has emerged as a transformative tool in healthcare, particularly drug and biomarker discovery, where it can enhance precision, streamline discovery processes, optimize treatment strategies. Despite its potential, the application of AI glioblastoma (GB) research, especially identifying novel biomarkers therapeutic targets, remains underexplored. The aim this review is to map existing literature on AI-driven approaches for GB, highlighting key trends gaps current research. Design: Following PRISMA methodology, scoping examined studies published between 2012 2024. Searches were conducted across multiple databases, including MEDLINE (PubMed), Scopus, Cochrane Library, Web Science (WOS). Eligible screened, relevant data extracted synthesized provide comprehensive overview applications GB Results: A total 224 records identified, 210 from PubMed, 104 4 WOS, 6 Library. After screening applying eligibility criteria, 33 included final review. These showcased diverse methodologies applied both identification, focusing various aspects biology treatment. Conclusions: This reveals an increasing interest strategies with promising initial results. However, further large-scale, rigorous are needed validate real-world development standardized protocols reproducibility clinical translation.

Язык: Английский

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