Bioprinting processes have greatly advanced in recent years through improvements in print accuracy and bioink optimization. Despite these advances, optimizing cell distribution and viability still relies on guess-and-check methods and destructive post-printing testing. The ability to monitor cells during printing would improve print quality and inform complex bioprinting processes, such as the generation of cellular gradients or controlled bioink transitions. Real-time monitoring using dielectric impedance spectroscopy (DIS) alleviates this burden by correlating impedance|Z|to cell properties. However, the influence of bioink properties on these measurements is unknown. Using an in-line impedance sensor, we assessed the effects of alginate bioink concentration, pH, and crosslinking on impedance over 1-25 000 kHz and determined how these properties influenced the detection of primary chondrocytes. In each scenario, impedance was highest in samples with low alginate concentration, low sample pH, or crosslinker. In nearly all samples, the addition of cells resulted in an increase in impedance compared to acellular samples, and this difference in impedance was used to quantify cell presence, termed |Zcells|. Higher alginate concentrations at 1 w/v% and 3 w/v% showed greater |Zcells|, indicating reliable cell detection. Although |Zcells| varied greatly with alginate or phosphate-buffered saline pH, similar measurements were found in pH resembling cell media. Optimal frequency ranges for monitoring acellular and cellular samples were from 10-100 kHz and 1000-25 000 kHz. Furthermore, cells were detected in real-time as acellular and cellular alginate bioinks were transitioned during bioprinting. This transition in cell concentration was spatially mapped to deposited bioink, providing a visual display of bioink transition using impedance. In summary, DIS detected cells suspended in alginate bioink and showed potential for real-time mapping of cell deposition.

Rapidin situbioprinting on complex, human-scale anatomical surfaces remains a key challenge for point-of-care use. Precise biomaterial ink or bioink deposition is required not only in operating theatres but also in resource-limited environments such as rural clinics and spaceflight missions. Here, we present a strategy for rapidly and conformally delivering biphasic biomaterial inks and bioinks composed of jammed gelatin microgels, optionally suspended in a cell-laden fibrinogen matrix. The formulation exhibits yield-stress behavior, preserves shape fidelity immediately after extrusion independent of gelation kinetics, maintains cell viability above 85%, and supports proliferation. The bioink is delivered through multinozzle printheads with 16 exit nozzles. During deposition at 450 mm2·s-1, a ladder-rung channel architecture provided more uniform area coverage compared with a bifurcated design. Two printhead configurations were investigated: (1) a pneumatically actuated soft-robotic printhead with real-time adaptation to physiologically relevant convex surface curvatures, and (2) a rigid printhead integrated with a handheld bioprinter that enabled the first demonstration of biphasic jammed biomaterial ink deposition in microgravity. Considered radii of curvature and gravitational accelerations ranged from 10-100 mm and 0-1 g, respectively. Together with fibrin network formation, these results establish a translationally relevant biofabrication framework forin situbioprinting in regenerative medicine, austere trauma care, and space-based healthcare.

The drug development process in cancer faces significant challenges due to high failure rates in translational studies despite promisingin vitroresults. Additionally, conventional animal models exhibit inherent limitations and ethical concerns, constraining their relevance to cancer studies. Recognizing the pivotal role of the tumor microenvironment (TME) on cancer development and treatment outcomes, recent advancements in 3D microfluidic devices and tumor-on-a-chip models enabled researchers to explore the TME with enhanced accuracy and reliability, yielding novel insights. Notably, the emergence of physiological tumor models, particularly 3D models such as organoids derived from human tissues, provides a more accurate representation ofin vivotumor features. Moreover, 3D tumor models hold promise for diverse applications, including high-throughput drug testing, disease modeling, and regenerative medicine. Meanwhile, combining artificial intelligence (AI) with patient-derived tumor organoids has become a key strategy in predictive oncology and personalized cancer treatment. Furthermore, incorporating quantitative systems pharmacology and physiologically based pharmacokinetic modeling, and pharmacokinetics/pharmacodynamics analysis with generative AI (Gen-AI) has revolutionized predictive oncology by enabling precise simulations of drug interactions and patient-specific responses, thereby enhancing the predictive accuracy of personalized cancer treatments. These advanced methodologies harness the power of AI algorithms to analyze intricate datasets derived from patient-specific tumor organoids. Moreover, the predictive modeling capabilities of Gen-AI facilitate the development of personalized treatment strategies customized for each patient, thereby revolutionizing oncology practice. This review explores the synergistic impact of tumor-on-a-chip models, organoids derived from patient tumors, and Gen-AI. Together, these technologies mark a significant advancement in precision medicine, offering promising opportunities to improve therapeutic effectiveness and treatment outcomes in cancer care.

The investigation of breast cancer initiation and progression has been significantly advanced by the development of three-dimensional (3D) in vitro models, which provide a more physiologically relevant representation of the tumor microenvironment (TME) compared to conventional two-dimensional cultures. Over the past decade, and particularly since 2020, a wide range of strategies has been developed to generate stable and functional 3D breast cancer models. This review provides a comprehensive overview of the most promising (bio)fabrication-based technologies developed for breast cancer modeling, critically discussing their applications, advantages, limitations, and future perspectives. Among current approaches, tumor spheroids have demonstrated considerable value due to their characteristic architecture, comprising a necrotic core surrounded by proliferative and quiescent cell layers, which partially mimics in vivo tumor organization. In parallel, organ-on-chip (OoC) systems have emerged as powerful platforms for drug screening and therapeutic testing, enabling dynamic culture conditions within microengineered and perfusable environments. However, spheroids lack an external extracellular matrix, while the compartmentalized nature of OoCs systems limits their ability to fully reproduce the structural and compositional complexity of the breast TME. To address these limitations, engineered 3D-printed scaffolds and constructs produced through biofabrication approaches have gained increasing attention. In particular, natural hydrogel-based systems offer high biocompatibility and tunable biochemical and mechanical properties, enabling the co-culture of malignant and healthy cells and supporting more predictive evaluations of anticancer therapies.

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.