Medical scientists seeking new ways to regenerate complex biological systems from cells have led to the advent of a 21st-century tissue engineering technology called 3D bio-printing. Without question, research on 3D bioprinting is new, disruptive, and expanding too. By taking advantage of AI, 3D bio-printing technology can only get better.  

An average of 20 persons dies waiting for an organ transplant every day, according to reports by The American Transplant Foundation. What if, instead of waiting, customized tissues and organs are created in-situ for every patient in need of one? This is the concept behind 3D bio-printing. 3D bioprinting of tissue and organ is a new field in the area of tissue engineering and regenerative medicine set to address the global problem of donor shortage faced by patients who are in dire need of an organ transplant.

To develop this cause, top-notch technologies from the areas of stem cell biology, biomaterials science, medicine, and engineering were integrated to create an inclusive and explosive bio-fabrication system. The goal is to produce artificially grown tissues that can replace damaged ones via a bottom-up approach. While the bladder remains the only organ that was bioprinted and successfully transplanted into a human, 3D bio-printing has led to the successful regeneration of several tissues, including –– bones, cartilages, cardiac tissues, liver cells, skin layers, tracheal splints, and vascular grafts for drug research purposes. The bio-fabrication of fully functional whole organs like the brain, heart, lung, and kidney that can be implanted into the human body is on the way. 

At first, the concept of fabricating living systems for humans seemed impossible, but when 3D printing, which was originally developed for non-biological applications by Charles Hull, found its way into the life-science stream, this automatically repositioned it as a ground-breaking innovation in the bio-fabrication of living systems. 3D bioprinting –– the sister-cousin of additive manufacturing, uses additive processes to fabricate bio-tissues in a layer-to-layer fashion from a digital 3D model using a combination of cells, growth factors, and biomaterials. No longer far from lacking the cellular functions that deem it fit to be a viable replacement, present-day 3D bioprinting constructs fully functional organ-specific tissues with size, functions, mechanical strength, and vasculature required for them to be used in drug discovery, organ replacement, organ-on-chip research, and wound healing.

Just like every new technology, 3D bioprinting is not without its challenges. And, when AI’s fast and smart algorithm is introduced into the 3D bioprinting workflow, it can –– reduce the risk of errors, fine-tune printing parameters, and expedite automated processes. Rahul Roy, the applications engineer at Cellink life sciences, a bioprinting company, states, “AI will certainly have a role in the future of bioprinting.” To identify viable areas for AI to thrive in this field, understanding the 3D bioprinting process is inevitable.


The process of bio-printing a tissue begins with defining what tissue needs to be printed, understanding how the tissue should function, and conceptualizing how to construct the tissue to display the desired functions. Once the tissue structure is defined, the 3D bio-printing protocol is mapped out to enable the clear-cut fabrication of the living tissue. As the living fibers are generated layer by layer, well-defined heterogeneous tissues are created. Bioprinting is impossible without the bio-ink, the bioprinter, and the bioprinting process. The type of bio-ink, print technology, and bioprinting form used is dependent on the cell of interest and the application of the printed tissue.

The Bioink               

A 3D bio-printer uses bio-ink –– a printable material composed of living cells from the patient’s body embedded in a bio-compatible material that acts as the 3D molecular scaffold. The bio-ink is then 3D printed into functional tissue construct either for transplantation, drug screening, or disease modeling. Bio-ink makes high-resolution 3D printing easy. The cell-friendly bio-ink also integrates living cells into microstructures during the printing process. This aids the bio functionality of the printed tissues. 

The Bioprinter 

The 3D bioprinter works the same way a 3D printer works but with a little twist. The bioprinter prints using 3D digital files as a blueprint while using cells and biomaterials as starting materials. 3D bioprinting also ensures that the tissues are well vascularized for the exchange of blood, oxygen, and other nutrients. 

The 3D Bioprinting Process

The 3D bio-printing process involves three stages.

  1. The Preparatory Stage

The process of printing a tissue starts with creating a digital design in a 3D modeling program. The 3D design-construct are sourced from biomedical diagnostic imaging techniques like computed tomography (CT), 3D laser scanning, or magnetic resonance imaging (MRI) scans directly from the patient. Anatomically precise 3D models are then designed using computer-aided design and computer-aided manufacturing (CAD/CAM) graphic software. 

The 3D model is loaded into a slicer. The slicer is a specially optimized type of computer program. It analyses the model geometry to produce a series of 3D stacks that will form the shape of the printed tissue. Once the model is sliced, they are stored as G-codes and sent off to a 3D bioprinter for printing. 

  1. The Bioprinting Stage

The G-codes are a series of commands that guide the bio-printer on how to fabricate the 3D tissue layer-to-layer. Depending on the tissue to be printed, the research scientist –– selects the bio-ink, mixes it with the tissue-specific cell, loads the cell-laden bio-ink into a cartridge, and places it into the printer. Once the printer receives data on control parameters from the G-codes, this sets the pace for the bioprinting stage. The bioprinting stage is completed once all the G-code commands are executed. 

  1. The Post Bioprinting Stage

After bioprinting, the fabricated constructs are stabilized by cross-linking. During the crosslinking process, the constructs can either be treated with an ionic solution or UV light, depending on the make-up. Having heterogeneous tissues with well-defined regions that are lacking in biological functions will thwart the 3D-bioprinting goal. Thus, to recreate the biological functions present in living cells, the structure of the bio-print is grown in specialized culture mediums under controlled conditions for some days.

During this maturation process, the printed cells form interconnected networks resulting in tissues with physiological potential. This procedure is important because it helps the tissue construct achieve the desired bio-physiological function. After the tissue has been produced, it is ready for use.


It is incontestable that the combination of two disruptive technologies can only lead to a world of unimaginable possibilities. 3D has made great progress in making functional bio-products available while step-forwarding the production of new assay models to forecast the effects of drugs in humans too. However, the challenges in the various stages of the 3D bioprinting workflow present exciting opportunities for AI. 

First, the biodegradable material used to maintain the shape of printed tissue can provoke an immune response and cause cell toxicity. Lucky enough, we have artificial immune systems that mimic human immune responses. Artificial immune systems can detect any anomaly or intrusive immunological response the tissue may elicit when implanted in the human body. AI can also predict outcomes of the various applications of 3D prints before use; this will reduce failure rates in clinical trials. 

Still, on biocompatibility, AI can give suggestions on the best possible printing parameters needed to produce a tissue that is biocompatible with a patient’s physiological makeup, by analyzing data and identifying patterns at every stage of the 3D bioprinting process. 

Second, most 3D bioprinting processes don’t scale through efficiently due to cellular damage during the bioprinting process. Cellular damage affects cellular interactions. So, there is the need to predict the maximum shear stress a cell can withstand and still retain its physiological potential. Integrating machine learning at the forefront of the bioprinter software and G-codes can suggest the perfect size of printer nozzle head to use in tuning the extrusion rate. By accurately fine-tuning all bioprinting parameters, AI can speed up printing time, increase resolution and eliminate the risk of contamination of the cells.

It’s no brainer that the future of AI in 3D printing is now as it offers exciting opportunities for AI experts, engineers, and medical researchers to exploit their expertise. However, there is so much to do with data in this area. Hence, generating big data, creating databases is essential if 3D bioprinting will fully harness the AI advantage. 

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