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Next generation bioprinting with artificial intelligence in the healthcare industry

This review maps how AI can support bioink selection, parameter optimisation, cell monitoring, digital twins and closed-loop bioprinting. Translation to care still depends on standardised data, reproducibility, biosafety and regulatory evidence.

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Key findings

  • This review maps how AI can support bioink selection, parameter optimisation, cell monitoring, digital twins and closed-loop bioprinting. Translation to care still depends on standardised data, reproducibility, biosafety and regulatory evidence.
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Why this matters globally

This work adds internationally comparable evidence in Health sciences and defines questions for replication in other populations or systems. Its global value lies in the evidence and transferable reasoning, not in a single impact score.

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Thai researcher contribution

Thailand-linked authors and King Mongkut's University of Technology North Bangkok contribute to the research network behind this work. Thai participation is identified from bibliographic affiliations and should be checked against the author list and source article.

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Limitations to consider

Conclusions inherit the quality, definitions and completeness of included studies; heterogeneity and publication bias can distort pooled impressions.

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Verify the original sources

Next BioengineeringRead the original article

DOI: 10.1016/j.nxbio.2026.100015

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