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Artificial intelligence in biobanking: current situation and future perspectives.
Casopis Lekaru Ceskych
|January 30, 2026
Summary
Artificial intelligence (AI) can enhance biobanks by improving data management and research. Integrating AI requires addressing technical, ethical, and data privacy challenges for personalized medicine.
Area of Science:
- Biomedical research
- Biobanking
- Artificial Intelligence
Background:
- Biobanks are crucial for biomedical research and personalized medicine.
- Growing volumes of diverse data (genomics, EHRs, imaging) offer research opportunities.
- Artificial intelligence (AI) can leverage this data for advancements.
Purpose of the Study:
- To review AI trends and challenges in biobanking.
- To discuss data management, biomarker discovery, and automation.
- To provide recommendations for AI integration in biobanks.
Main Methods:
- Literature review of current AI applications in biobanks.
- Analysis of technical and ethical considerations.
- Examination of GDPR, FAIR data principles, and federated learning.
Main Results:
- AI offers significant potential for biobank data management and research.
- Key challenges include data heterogeneity, ethics, and privacy (GDPR).
- FAIR data principles and federated learning are vital for AI integration.
Conclusions:
- AI integration in biobanks requires careful planning and ethical oversight.
- Explainable AI (XAI) is important for trust and transparency.
- Recommendations are provided for sustainable AI implementation in the Czech healthcare system.
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