Evidence mapPaperPMID 41512329Full record

ReviewBiofabrication2026

Self-driving bioprinting laboratories.

Suihong Liu, Navneet Kaur, Dae-Hyeon Song, Joseph Christakiran Moses, Ibrahim T Ozbolat

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Suihong LiuThe Huck Institutes of Life Sciences, Penn State University, University Park, State College, PA 16802, United States of America.ORCID 0000-0001-5503-2980
Navneet KaurThe Huck Institutes of Life Sciences, Penn State University, University Park, State College, PA 16802, United States of America.ORCID 0000-0003-4543-370X
Dae-Hyeon SongThe Huck Institutes of Life Sciences, Penn State University, University Park, State College, PA 16802, United States of America.ORCID 0000-0002-9784-4298
Joseph Christakiran MosesThe Huck Institutes of Life Sciences, Penn State University, University Park, State College, PA 16802, United States of America.ORCID 0000-0001-9794-8196
Ibrahim T OzbolatThe Huck Institutes of Life Sciences, Penn State University, University Park, State College, PA 16802, United States of America.ORCID 0000-0001-8328-4528

Funding

Developing a bioprinted ventilated lung alveolar platform for investigating microbial interactions and influenza responseR01AI186386 · DUKE UNIVERSITY · 2025 to 2025
$3.2M
Technology Development Project - Increasing the complexity of ex vivo human airway models for studying immune response to viral infectionU19AI142733 · JACKSON LABORATORY · 2025 to 2025
$2.6M
Intraoperative bioprinting of composite tissues with zonal stratification for craniomaxillofacial reconstructionR01DE028614 · NIDCR · PENNSYLVANIA STATE UNIVERSITY, THE · PI Ibrahim Ozbolat · 2022 to 2024
$1.7M
Leveraging Microsurgery and Bioprinting for Rapidly Oriented Vascularized Tissue EngineeringR01DE035200 · PENNSYLVANIA STATE UNIVERSITY, THE · 2025 to 2025
$686k
3D Printing of Air: An Intangible Ink for Fabrication of Vascularized TissuesR01EB036245 · PENNSYLVANIA STATE UNIVERSITY, THE · 2025 to 2025
$572k
NIAID NIH HHS R01 AI186386NIAID NIH HHS U19 AI142733NIAMS NIH HHS R21 AR082668NIBIB NIH HHS R01 EB036245NIDCR NIH HHS R01 DE028614NIDCR NIH HHS R01 DE035200
6 · The paper itself

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

BioprintingLaboratoriesAnimalsArtificial IntelligenceHumansPrinting, Three-DimensionalRegenerative MedicineRoboticsTissue Engineeringartificial intelligencebioprintingbioreactorroboticsself-driving laboratories

Identifiers

PMID41512329
PMCPMC12824512

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.