Artificial intelligence in tissue engineering and regenerative medicine: from algorithmic capability to intelligent integration
TL;DR
A review of AI’s use as a framework for knowledge extraction, experimental planning, decision support, experimental execution, data generation, interpretation, and scientific communication in tissue engineering and regenerative medicine concludes that many important gaps in AI-enabled TERM are not algorithmic but conceptual, methodological, and educational.
Abstract
Artificial intelligence (AI) is increasingly integrated throughout the tissue engineering and regenerative medicine (TERM) pipeline, from literature synthesis, experimental design, and automation to image analysis, biological modeling, and knowledge dissemination. However, TERM differs fundamentally from many domains where AI has succeeded. Biological heterogeneity, limited datasets, evolving protocols, sparse ground truth, and high translational stakes constrain the development and deployment of AI-driven approaches. Consequently, AI should be seen as a collection of purpose-specific tools whose suitability, risks, and validation requirements depend on the context. This review examines how AI can be intelligently integrated across the TERM workflow, emphasizing how intended purpose, biological reality, and translational constraints shape technology selection. We examine AI’s use as a framework for knowledge extraction, experimental planning, decision support, experimental execution, data generation, interpretation, and scientific communication. Key challenges include limited generalizability, uncertainty, validation, accountability, and the risk of confusing predictive performance with biological or mechanistic insight. We argue that many important gaps in AI-enabled TERM are not algorithmic but conceptual, methodological, and educational. Future progress will depend on purpose-aware AI literacy, uncertainty-aware modeling, validation strategies, human-in-the-loop workflows, and establishing community standards for transparency, reproducibility, and translation. Ultimately, AI should not be a shortcut around biological complexity, but a means of engaging with that complexity more explicitly and responsibly. Across the TERM pipeline, AI is best viewed as an amplifier of biological evidence and scientific reasoning rather than a substitute, with its greatest impact arising from intelligent integration into scientific practice rather than increasingly intelligent algorithms alone.