ReviewJournal of personalized medicine2023
Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking.
Review in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Harnessing human tumor organoids for cancer modeling and precision therapy.Protein & cell · 2026Review
- Microbial biobanking: safeguarding the tiny treasures for sustainable human welfare.Folia microbiologica · 2026Review
- Establishing a Cervical Cytology Biorepository: A Protocol for Advancing Translational Cervical Cancer Research through Biobanking.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Governing synthetic biology and artificial intelligence (AI) convergence: emerging biosecurity priorities for Africa.Frontiers in bioengineering and biotechnology · 2026Review
- Unlocking personalized endometrial cancer treatment: the critical role of the BBIRE biobank in sample collection and distribution.Frontiers in molecular biosciences · 2026Article
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- An interpretable hybrid deep learning framework for gastric cancer diagnosis using histopathological imaging.Scientific reports · 2025Article
- Enhanced digital pathology image recognition via multi-attention mechanisms: the MACC-Net approach.Scientific reports · 2025Article
- Liquid biopsy in cancer management: Integrating diagnostics and clinical applications.Practical laboratory medicine · 2025Review
- The Regulatory Landscape of Biobanks In Europe: From Accreditation to Intellectual Property.Current genomics · 2025Review
- How the world of biobanking is changing with artificial intelligence.Frontiers in digital health · 2025Review
- Deep learning algorithm on H&E whole slide images to characterizeComputational and structural biotechnology journal · 2024Article
- Closing Editorial: Colorectal Cancer-A Molecular Genetics Perspective.International journal of molecular sciences · 2024Article
- Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care.Life (Basel, Switzerland) · 2024Review
- The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.JMIR medical education · 2024Article
- Data Management in Biobanking: Strategies, Challenges, and Future Directions.Biotech (Basel (Switzerland)) · 2024Review
- Biobanks in chronic disease management: A comprehensive review of strategies, challenges, and future directions.Heliyon · 2024Review
- Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.Cancers · 2024Review
- Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine.Journal of translational medicine · 2024Review
- Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.Healthcare (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research.
methodsIn this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis.
resultsThese cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies.
conclusionsThe integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.