Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.

3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.

Treatments for osteochondral defects (OCDs) are mainly palliative and, with the increase in this pathology seen among both young and elderly people, an alternative treatment modality is sought. Many tissue-engineered strategies have been explored for regenerating the cartilage–bone interface; however, they generally fall short of being ideal. Although cell-laden hydrogel scaffolds are a common approach for bone and cartilage tissue regeneration, they usually lack homogenous cell dispersion and patient specificity. In this study, a biphasic 3D bioprinted composite scaffold was fabricated for cartilage–bone interface regeneration. To overcome the shortcoming of both materials, alginate–gelatin (A–G) hydrogel was used to confer a naturally occurring environment for the cells and polycaprolactone (PCL) was used to enhance mechanical stability, thus maximizing the overall performance. Hydroxyapatite fillers were added to the PCL in the bone phase of the scaffold to improve its bioactivity. Physical and biological evaluation of scaffolds in both phases was assessed. The scaffolds demonstrated a desirable biological response both singly and in the combined PCL/A-G scaffolds, in both the short term and longer term, showing promise as an interfacial material between cartilage and bone.

Acoustic techniques have emerged as a transformative approach in additive manufacturing, enabling contactless, high-precision manipulation of particles, droplets, and cells through acoustic wavefields. While acoustic patterning has demonstrated remarkable control over microscale spatial configurations, its scalability into three-dimensional (3D) fabrication has remained constrained by challenges in bonding limitations and vertical stacking. To solve this challenge, hybrid acoustic-assisted 3D printing integrates acoustophoresis with established methods, such as direct ink writing and stereolithography, enhancing material deposition accuracy, microstructure alignment, and porous structuring of 3D printed products. However, these hybrid systems remain tethered to layer-by-layer architectures, limiting the full potential of acoustic techniques. Recent breakthroughs in standalone acoustic 3D fabrication, leveraging levitation, focusing, and ejection, have unlocked potential in contactless, layerless, and flexible multi-material assembly. Acoustic levitation systems enable mid-air construction on non-planar surfaces, and focused ultrasound techniques facilitate deep-penetration polymerization and acoustic droplet ejection advances nozzle-free droplet-based bioprinting. This review systematically evaluates the evolution of acoustics in additive manufacturing, addressing critical challenges in material compatibility, resolution, and scalability, while outlining the future of acoustics in additive manufacturing technology.

Polyetheretherketone (PEEK) is a thermoplastic polymer that has been recently employed for bone tissue engineering as a result of its biocompatibility and mechanical properties being comparable to human bone. PEEK, however, is a bio-inert material and, when implanted, does not interact with the host tissues, resulting in poor integration. In this work, the surfaces of 3D-printed PEEK disks were functionalized with: (i) an adhesive peptide reproducing [351–359] h-Vitronectin sequence (HVP) and (ii) HVP retro-inverted dimer (D2HVP), that combines the bioactivity of the native sequence (HVP) with the stability toward proteolytic degradation. Both sequences were designed to be anchored to the polymer surface through specific covalent bonds via oxime chemistry. All functionalized PEEK samples were characterized by Water Contact Angle (WCA) measurements, Atomic Force Microscopy (AFM), and X-ray Photoelectron Spectroscopy (XPS) to confirm the peptide enrichment. The biological results showed that both peptides were able to increase cell proliferation at 3 and 21 days. D2HVP functionalized PEEK resulted in an enhanced proliferation across all time points investigated with higher calcium deposition and more elongated cell morphology.