ReviewBiofabrication2026
Self-driving bioprinting laboratories.
Review in Biofabrication, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- The Next Phase of 3D Bioprinting: AI-Native Systems-A Narrative Review.Journal of functional biomaterials · 2026Review
- Bioinks in systems biology: integrating biomaterial design with cellular network modelling for predictive biofabrication.Frontiers in bioengineering and biotechnology · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The severe shortage of donor organs and limitations of current disease models highlight the urgent need for transformative strategies in tissue engineering (TE) and regenerative medicine (RM). Bioprinting has emerged as a powerful approach for creating functional tissues and organs, yet current workflows remain labor-intensive, variable, and challenging to scale. The convergence of artificial intelligence (AI), advanced bioprinting technologies, robotics, biosensing, and cutting-edge biological methods is catalyzing the development of self-driving bioprinting laboratories-a fully integrated, autonomous, closed-loop system capable of designing, fabricating, maturing, and assessing living tissue constructs, as well as supporting seamless transplantation, with minimal human intervention. By integrating autonomous cellular farming, on-demand bioink formulation, intelligent optical and digital reconstruction platforms, AI-driven bioprinting, intelligent bioreactors, and robotic transplantation within a sterile, interconnected ecosystem, such platforms can continuously learn, adapt, and optimize workflows, enabling standardized, scalable tissue manufacturing and facilitating a seamless transition from bench to bedside. This perspective outlines the foundational technologies, opportunities, and challenges for realizing self-driving bioprinting, envisioning a future where intelligent, automated platforms transform TE and RM into a scalable, predictive, and clinically integrated discipline at the forefront of precision medicine.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.