Across surgical specialties, minimally invasive (laparoscopic) surgery has become a standard technique, as it is associated with less trauma, reduced postoperative complication rates, and quicker recovery for patients as compared with open surgery. Due to the limited field of view and limited haptic feedback, the surgical decision-making process in laparoscopic surgery is currently guided solely by surgeons' visual interpretation of the laparoscopic video stream. Modern artificial intelligence (AI) methods excel at the interpretation of visual data and find applications in clinical routine in fields such as radiology and endoscopy. AI methods could help augment laparoscopic surgery through objective real-time analysis of the laparoscopic video stream. Research studies have demonstrated the feasibility of AI-based surgical scene and process understanding. This review provides an overview of these AI applications, focusing on approaches that could, in the next decade, be translated into intraoperative surgical decision support tools for increased surgical quality and patient safety.
Digital twins—virtual representations dynamically linked to physical systems—have the potential to transform biomedical engineering by enabling real-time prediction, optimization, and personalization in health and disease. In biofluids, digital twins offer a framework for integrating physics-based models with data from clinical imaging, sensors, and physiological measurements to support diagnostics, therapeutic planning, and device design. This article reviews modeling approaches used in the construction of digital twins for biofluid applications. We survey high-fidelity numerical methods alongside emerging machine learning techniques, highlighting their respective strengths and limitations. Key requirements for digital twins are discussed, emphasizing the bidirectional interaction between physical and virtual assets, and the importance of selecting modeling strategies tailored to specific biomedical contexts. While notable progress has been made over the past decade, significant challenges remain, particularly in integrating multiphysics models with data-driven methods and in establishing standardized protocols for data acquisition, interoperability, and sharing.
As biomaterials transition from static, inert objects into dynamic scaffolds that augment the healing process, the ability to design large-scale changes in architecture in response to environmental changes is increasingly valuable. To address this need, stimuli-responsive, or “smart,” biomaterials are employed in clinical applications more frequently. These materials are capable of undergoing large and abrupt changes in shape in response to small changes in the environment, such as temperature, pH, mechanical forces, light, magnetic fields, or enzymes. Smart polymeric biomaterials include shape memory polymers, stimuli-responsive hydrogels, and liquid crystal elastomers. These materials are used in a vast range of biomedical applications, including embolic/hemostatic devices, orthopedics, cosmetics, wound healing, drug delivery, neural scaffolds, and infection control, among others. While smart materials can be employed in vitro for biomedical uses, we focus here on materials that have been designed for use in or on the human body. The signals that these materials respond to include those that occur endogenously in vivo and externally applied signals that are controlled by clinicians and/or patients. This review highlights current clinically available devices based on smart materials, smart polymeric biomaterial technologies that are in current clinical trials, and novel materials that are under development for future clinical use.
Cancer therapies such as chemotherapy, radiopharmaceutical therapy, and transarterial embolization rely on effective drug or radiation delivery through the bloodstream. Understanding how various drugs and particles, which form and size span multiple scales, are transported through blood and tissue is essential for optimizing treatment. Computational fluid dynamics (CFD) is a powerful tool to simulate blood flow and drug transport, solving flow governing equations under biologically realistic conditions. This review explores CFD applications in cancer therapy, focusing on transarterial embolization, tumor perfusion, and organ-on-a-chip systems. In radioembolization, CFD can predict microsphere transport and dose distribution to spare vital functions. Tumor perfusion modeling and organ-on-a-chip systems benefit from CFD by replicating vascular dynamics and drug dispersion. Despite its versatility and established mechanical principles, CFD faces challenges, including the need for patient-specific data, computational demands, and multiscale modeling. This review highlights opportunities for integrating CFD with imaging modalities and artificial intelligence tools to overcome these barriers and advance personalized cancer treatment.
Tendons enable movements by transmitting forces from muscle to bone, requiring highly specialized material properties for their mechanically demanding functions. Unfortunately, tendon material properties are frequently compromised by physical overuse or genetic aberrations. Lysyl oxidase (LOX)-mediated collagen cross-linking, which is governed by collagen posttranslational modifications (PTMs), is a critical regulator of tendon material properties. Despite studies implicating roles for LOX-mediated cross-linking and underlying PTMs in tendon material properties, these mechanisms are not well understood in tendons in birth disorders, aging, or healing from injury. Here, we review what is known regarding collagen PTMs that influence LOX-mediated collagen cross-linking in tendon and their known contributions to tendon material properties, and we highlight critical gaps in our understanding of these mechanisms. With a more thorough understanding of mechanisms responsible for LOX-mediated collagen cross-linking in tendons, we could exploit these mechanisms to improve the material properties of tendons affected by injury, aging, or congenital disorders.
Cells exist along a spectrum from viable to dead. Yet most cell engineering has focused primarily on the live state. This review explores how controlled manipulation of cell death or arrest can be used to build effective, safer, and more predictable therapies. By engineering apoptosis, irreversible growth arrest, or synthetic gene circuits, cells can be programmed for functional outputs without relying on full viability. These approaches reduce heterogeneity, improve stability, and extend the therapeutic window. We introduce a framework for understanding engineered living, dying, and dead cell therapies on the basis of their activity, functions, and applications. Across this spectrum, engineered cells show promise for applications in immune modulation, drug delivery, and tissue regeneration. We also examine key methods that enable these designs, including genetic, physical, and materials-based tools. This perspective offers a path toward programmable and consistent cell-based therapies across diverse biomedical domains.
The continuous, real-time measurement of specific molecules in situ in the body promises to revolutionize the precision of drug dosing, the reach of physiological research, and the accuracy and timeliness of clinical diagnostics. Motivated by these prospects, here we critically review the set of molecular sensing technologies that ( a ) have been demonstrated to support seconds-resolved measurements in mammalian subjects and ( b ) appear translatable to the clinic. The relevant technologies employ four readout modalities: ( a ) direct or enzymatic electrochemical detection, ( b ) indirect electrochemical detection, ( c ) optical strategies, and ( d ) photoacoustic sensing. For each, we analyze the engineering requirements to support real-time, in vivo operation—which include reversibility, reagentless interrogation, and selectivity—and the remaining barriers to clinical adoption, including sensitivity and long-term in vivo stability and biocompatibility. Collectively, the advances surveyed here suggest that seconds-resolved molecular monitoring in humans is within reach and will herald a new era of precision diagnostics and closed-loop therapeutics.
Light-sheet fluorescence microscopy (LSFM) has emerged as a revolutionary imaging modality for investigating intact three-dimensional brain structures at the teravoxel scale. In parallel, high-throughput computational methods, especially deep learning approaches, have opened new avenues for uncovering the pathophysiological mechanisms of neurological diseases through LSFM technology. Recent advances in optics and tissue clearing methods have allowed whole-brain imaging at cellular resolution in three dimensions, and the integration of artificial intelligence has facilitated the identification of disease-related cellular profiles and morphological markers. Machine learning techniques for stitching, segmentation, classification, super-resolution, and registration, therefore, are promoted to uncover biological patterns that are not visible to human eyes yet are related to neuroinflammatory and neurodegenerative diseases. However, analytic pipelines have been designed differently for various animal models and brain structures, leading to challenges in feasibility and compatibility within this emerging field of data-driven LSFM image analysis. Here, we present an overview of current pipelines, examine existing and forthcoming challenges as the LSFM community advances, demonstrate their implications for neurological disease applications, and propose potential solutions.
More than 90% of central nervous system-directed drug candidates fail because they never reach the brain, and similarly formidable physiological tissue barriers protect the placenta and lymph node. These tissues are walled off by tight junctions, efflux pumps, and immune sentinels that thwart both endogenous and exogenous agents, complicating disease treatment. Here, we detail the microanatomy, transport mechanisms, and pathological shifts of the blood-brain, blood-lymph node, and maternal-fetal barriers. We then benchmark emerging solutions, from receptor-targeted nanoparticles and stimulus-responsive carriers to cell-mediated and bioderived vehicles, against the current landscape of US Food and Drug Administration-approved and experimental therapeutics. By comparing the relatively advanced blood-brain barrier delivery techniques to the immature landscape of lymph node and placental drug delivery, we can advance investigation of these tissues quicker. Leveraging tissue-specific physiology promises higher therapeutic indices, lower systemic toxicity, and faster clinical success and illuminates the priority questions that must be answered over the next decade.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent hepatic pathology worldwide, with significant potential for progression to cirrhosis and ultimately end-stage liver disease. Accordingly, a wide range of preclinical models have been developed to better understand the disease mechanisms and progression as well as to accelerate drug discovery. These include in vitro, ex vivo, and in vivo models, which offer unique advantages yet differ in terms of disease driver, species used, and biological complexity-ranging from benchtop cellular systems to whole organs and organisms. In this review, we provide a comprehensive overview of the technologies currently used for the study of MASLD, with a focus on how standardization of disease progression across models may aid therapeutic development.
Type 1 diabetes (T1D) is a chronic condition in which patients suffer from high blood glucose levels due to the body's inability to produce sufficient insulin. Continuous insulin administration and T1D management are difficult, often leading to hypoglycemic events, insulin resistance, and lower quality of life. Major advancements have been made in recent years, including clinical islet transplantation, but their application is limited by rapid immune rejection and islet destruction. Thus, a necessary paradigm shift has been observed in recent times toward biomaterial-based islet transplantation therapy. The use of biomaterial-based encapsulation addresses major limitations, including immune rejection and hypoxia, and provides a proper cell microenvironment offering greater islet viability. Presently, researchers are more focused on developing a clinically translatable therapy for T1D with the existing knowledge of advanced biomaterial technology. In this review article, we provide a historical perspective, highlighting the developments in the field of islet encapsulation and transplantation, and focus on cutting-edge advancements with modern bioengineering from a clinical perspective.
Tumors display genomic and phenotypic heterogeneity, which holds prognostic significance and may influence therapy response. Radiographic imaging modalities, such as computed tomography, magnetic resonance imaging, nuclear medicine techniques, and ultrasonography, are routinely used to generate parametric maps to identify, measure, and map tumor heterogeneity from different perspectives encompassing anatomy, physiology, and metabolism. This review underscores the potential of artificial intelligence (AI)-based habitat imaging analysis, referred to as Radiomics++, in decoding intratumor heterogeneity compared to conventional radiomics. We highlight the general workflow, underlying principles, detailed methodology, and clinical applications of habitat imaging analysis to guide researchers. Validation advancements are then reviewed to verify the reliability of generated habitats by correlating radiologic phenotypes with biologic underpinnings. Furthermore, we address key challenges and opportunities in clinical translation, including data heterogeneity, model performance, and interpretability. Finally, integrating AI-defined habitats with multi-omics is anticipated to deepen our understanding of tumor evolution and advance precision medicine.
Acrolein is a highly reactive α,β-unsaturated aldehyde produced endogenously through lipid peroxidation and enzymatic metabolism and exogenously via environmental exposures. Acrolein covalently adducts to DNA and proteins, leading to oxidative stress, mitochondrial dysfunction, and inflammation, including innate immune response activation via natural antibodies. Acrolein is difficult to measure in biological systems, but its covalent products can be measured reliably. Therapeutically, nucleophilic small molecules that scavenge acrolein such as hydralazine, phenelzine, dimercaprol, carnosine, and N-acetylcysteine (NAC) have shown neuroprotective effects in animal models of multiple sclerosis, Parkinson's disease, spinal cord injury, and traumatic brain injury. These effects include preserved membrane and mitochondrial integrity, reduced inflammation, reduced pain, and improved motor, sensory, and cognitive outcomes. Alternative strategies that enhance clearance or inhibit production of acrolein show promise but face limitations. Acrolein is a key pathophysiological mediator and a viable therapeutic target in central nervous system trauma and neurodegenerative diseases.
Artificial intelligence (AI), including deep and traditional machine learning, holds great promise for advancing biomedical research and healthcare. However, most AI studies remain academic in nature and rarely transition into clinical practice, largely due to limited access to diverse real-world datasets. Centralized learning, the traditional approach to multi-institutional collaboration, is hindered by privacy, legal, and logistical barriers. Federated learning (FL) offers a decentralized alternative, enabling institutions to collaboratively train models without sharing sensitive patient data. This article reviews key algorithmic, privacy, and practical developments in FL for biomedical engineering, including strategies to handle non-identical data distributions and safeguard privacy through differential privacy, secure aggregation, and confidential computing. We also discuss current limitations and considerations for the need of scalable, interoperable infrastructures. FL represents a paradigm shift toward building generalizable, equitable, and clinically impactful AI models. Realizing this vision requires continued advances, such as FL-as-a-service platforms and regulatory-aligned workflows that support persistent and trustworthy model deployment to truly realize AI's promise in patient care.
Precision neurotherapeutics represents a transformative paradigm shift from standardized "one-size-fits-all" treatments of neurological, neurodegenerative, and/or psychiatric disorders toward individualized interventions that leverage patient-specific biological, behavioral, and physiological characteristics. Traditional neurotherapeutic approaches achieve modest response rates of 30-60% for first-line treatments, necessitating personalized strategies that account for individual differences in genetics, brain structure and function, and treatment response profiles. This review examines advances across three core domains: pharmaceutical approaches utilizing fragment-based drug discovery, pharmacokinetic modeling, and quantitative systems pharmacology; neuromodulation technologies evolving from open-loop to adaptive closed-loop systems with real-time biomarker feedback; and biomarker development spanning neuroimaging, pharmacogenomics, and digital health applications. Critical challenges include developing robust methodological frameworks for single-subject parameter estimation, addressing signal-to-noise ratio limitations in neuroimaging, and navigating complex regulatory landscapes. The convergence of artificial intelligence, computational modeling, and US Food and Drug Administration policy shifts toward in silico approaches creates unprecedented opportunities for mechanistically informed biomarkers that can guide truly personalized mental health care.
As health care systems worldwide seek to decentralize diagnostics and expand precision medicine, silicon photonic biosensors have become a compelling solution. Their development over the past decade, especially in the last 5 years, marks a significant convergence of photonics, nanotechnology, and biomedical engineering that aims to reshape the diagnostic landscape. This review presents a comprehensive analysis of advances in silicon photonic biosensors, focusing on key configurations including microring resonators, photonic crystals, interferometers, and other emerging transduction mechanisms. We discuss the integration of advanced surface functionalization strategies for efficient and robust bioreceptor immobilization, which is critical for reliable biomedical applications. We emphasize the translation of these devices into clinical settings, primarily in infectious diseases and cancer diagnostics. Finally, we address current limitations, such as fabrication complexity, microfluidic integration, and data interpretation, and outline future directions to enhance scalability and clinical adoption in personalized medicine and decentralized health care.
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
We review how sensorimotor control is dictated by interacting neural populations, optimal feedback mechanisms, and the biomechanics of bodies. First, we outline the distributed anatomical loops that shuttle sensorimotor signals between cortex, subcortical regions, and spinal cord. We then summarize evidence that neural population activity occupies low-dimensional, dynam-ically evolving manifolds during planning and execution of movements. Next, we summarize literature explaining motor behavior through the lens of optimal control theory, which clarifies the role of internal models and feedback during motor control. Finally, recent studies on embodied sensorimotor control address gaps within each framework by aiming to elucidate neural population activity through the explicit control of musculoskeletal dynamics. We close by discussing open problems and opportunities: multitasking and cognitively rich behavior, multiregional circuit models, and the level of anatomical detail needed in body and network models. Together, this review and recent advances point toward reaching an integrative account of the neural control of movement.
Over the last decade, a plethora of organoid models have been generated to recapitulate aspects of human development, disease, tissue homeostasis, and repair. Organoids representing multiple tissues have emerged and are typically categorized based on their origin. Tissue-derived organoids are established directly from tissue-resident stem/progenitor cells of either adult or fetal origin. Starting from pluripotent stem cells (PSCs), PSC-derived organoids instead recapitulate the developmental trajectory of a given organ. Gene editing technologies, particularly the CRISPR-Cas toolbox, have greatly facilitated gene manipulation experiments with considerable ease and scalability, revolutionizing organoid-based human biology research. Here, we review the recent adaptation of CRISPR-based screenings in organoids. We examine the strategies adopted to perform CRISPR screenings in organoids, discuss different screening scopes and readouts, and highlight organoid-specific challenges. We then discuss individual organoid-based genome screening studies that have uncovered novel genes involved in a variety of biological processes. We close by providing an outlook on how widespread adaptation of CRISPR screenings across the organoid field may be achieved, to ultimately leverage our understanding of human biology.
With increasing demands for continuous health monitoring remotely, wearable and implantable devices have attracted considerable interest. To fulfill such demands, novel materials and device structures have been investigated, since commercial biomedical devices are not compatible with flexible and conformable form factors needed for soft tissue monitoring and intervention. Among various materials, piezoelectric materials have been widely adopted for multiple applications including sensing, energy harvesting, neurostimulation, drug delivery, and ultrasound imaging owing to their unique electromechanical conversion properties. In this review, we provide a comprehensive overview of piezoelectric-based wearable and implantable biomedical devices. We first provide the basic principles of piezoelectric devices and device design strategies for wearable and implantable form factors. Then, we discuss various state-of-the-art applications of wearable and implantable piezoelectric devices and their design strategies. Finally, we demonstrate several challenges and outlooks for designing piezoelectric-based conformable biomedical devices.
