Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.

Publisher: Blackwell Country of Publication: England NLM ID: 9504453 Publication Model: Print Cited Medium: Internet ISSN: 1600-0846 (Electronic) Linking ISSN: 0909752X NLM ISO Abbreviation: Skin Res Technol Subsets: MEDLINE

Publication: <1998-> : Oxford : Blackwell
Original Publication: Copenhagen, Denmark ; Cambridge, MA : Munksgaard, c1995-

Regenerative Medicine*/methods ; Tissue Engineering*/methods ; Artificial Intelligence*; Humans ; Tissue Scaffolds ; Machine Learning

Background: Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role.
Methods: The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated.
Results: The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM.
Conclusion: The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside.
Highlights: The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
(© 2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.)

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3867 Semnan University of Medical Sciences

Keywords: artificial intelligence; biomaterials; machine learning; regenerative medicine; tissue engineering

Date Created: 20240827 Date Completed: 20240827 Latest Revision: 20240829

20240830

PMC11348508

10.1111/srt.70016

39189880