Focused ultrasound combined with intravenously infused microbubbles has been shown to effectively enhance the permeability of the blood-brain barrier, facilitating drug delivery to the brain. A wide range of technical parameters has been evaluated through preclinical studies and clinical trials. Generally, a low frequency between 200 and 300 kHz is preferred for the transcranial approach, while 1 MHz is used in implantable devices. Standard parameters include a burst length of 5 to 10 ms, a pulse repetition frequency of 0.2 to 10 Hz, and sonication durations of 90 to 180 seconds. A pressure magnitude around 0.46 mechanical index appears to be near the threshold for BBB permeability enhancement at standard microbubble dosage without causing hemorrhage. Various microbubble and nanobubble types have been tested at different doses, which in principle can be normalized by gas volume. Control methods that use harmonic emmisions for power feedback have been proposed to enhance consistency and account for patient variability, and these methods are currently being tested in several clinical trials.

Human neuroscience is undergoing a paradigm shift from traditional lab settings to natural environments. Functional Near Infrared Spectroscopy (fNIRS) and its variant, High-Density Diffuse Optical Tomography (HD-DOT) are rapidly evolving techniques that are increasingly adopted across disciplines. The high ease of use of advanced systems can enable continuous brain monitoring and thus the acquisition of large amounts of data. Integrating these data with modern deep learning (DL) promises to offer robust and generalizable solutions to ongoing challenges in fNIRS-related domains. As DL is a rather new field in fNIRS, we conduct a method-focused review, discussing 100 papers in the context of architectures, applications, and learning strategies. Based on the limitations in literature and the research gap between fNIRS and other domains, we conduct a tutorial study with guidelines from the wider DL field. We focus on: straightforward pre-processing pipelines; the trade-off between available data and model complexity of different architectures, including transformers; the generalizability of models for unseen data; and explainability. Finally, we provide a problem-focused discussion, gathering essential problems in the community, and introduce advanced DL solutions. This review serves as a strategic guide for advancing the current methodology for DL approaches in the fNIRS field.

Optogenetics has emerged as a pivotal tool in neuroscience, enabling intricate modulation of targeted neurons within the nervous system. Despite its transformative potential, achieving high spatiotemporal resolution in neuromodulation remains a significant challenge, particularly in free-behaving animals. This review aims to highlight recent advances in optogenetic systems for neuromodulation, focusing on the efforts to achieve superior precision in spatiotemporal control. We provide a comprehensive overview of the breakthroughs in optogenetic tools that offer ultrafast responsiveness, strategies for targeted tissue- and cell-specific optogene delivery, and methods for precise optical stimulation with minimal impact on the behavior of subjects. Additionally, we review the applications of optogenetics in neurological diseases, emphasizing its potential to advance therapeutic interventions. These innovations are poised to propel optogenetics into a new era, accelerating its clinical translation for precision neuromodulation and treatment of neurological disorders.

Transcranial focused ultrasound (tFUS) is an emerging neuromodulation and therapeutic technology offering noninvasive, submillimeter precision for targeting deep brain structures. Unlike transcranial magnetic stimulation (TMS) and transcranial electric stimulation (tES), which are limited by depth-focality tradeoffs, or deep brain stimulation (DBS), which is invasive and costly, tFUS enables precise modulation with minimal risk. Its applications include ablation for movement and psychiatric disorders, blood-brain barrier opening (BBBO) for drug delivery in neuro-oncology and neurodegeneration, and neuromodulation for circuit-based interventions in addiction, mood/anxiety disorders, and chronic pain. Advances in phased-array transducers, holographic focusing, and real-time imaging continue to refine its accuracy and safety. Ongoing research explores closed-loop systems and wearable devices to expand clinical accessibility. This review outlines the physics, current applications, and future directions of tFUS, positioning it as a transformative tool in personalized neuromodulation and neurotherapeutics.

Neurorehabilitation conventionally relies on the interaction between a patient and a physical therapist. Robotic systems can improve and enrich the physical feedback provided to patients after neurological injury, but they under-utilize the adaptability and clinical expertise of trained therapists. In this position paper, we advocate for a novel approach that integrates the therapist's clinical expertise and nuanced decision-making with the strength, accuracy, and repeatability of robotics: Robot-mediated physical Human-Human Interaction. This framework, which enables two individuals to physically interact through robotic devices, has been studied across diverse research groups and has recently emerged as a promising link between conventional manual therapy and rehabilitation robotics, harmonizing the strengths of both approaches. Although current findings are largely based on pilot studies and conceptual frameworks, integrating therapists' expertise with the functionalities offered by robotic systems represents a promising direction for improving rehabilitation outcomes. This paper presents the rationale of a multidisciplinary team-including engineers, doctors, and physical therapists-for conducting research that utilizes: a unified taxonomy to describe robot-mediated rehabilitation, a framework of interaction based on social psychology, and a technological approach that makes robotic systems seamless facilitators of natural human-human interaction.

The Robotic Ultrasound System (RUSS) has the potential to transform medical imaging by addressing limitations such as operator dependency, diagnostic variability, and reproducibility in traditional ultrasound (US) examination. Despite rapid technological advancements, a substantial gap remains between RUSS research progress and clinical adoption. This review examined the clinical roles and engineering advances of RUSS, identifying key barriers to translation. Clinically, it evaluated the current applications of RUSS in supporting US procedures, while from an engineering standpoint, it summarized recent innovations and remaining technical challenges. This review examined the current state-of-the-art RUSS technologies, categorizing them based on diverse organ-specific applications while also analyzing their core functional capabilities. This review revealed a focus disparity: while abdominal US is the most commonly used in clinical practice, vascular-targeted RUSS dominates current research. It also highlighted a misalignment between research priorities and actual clinical tasks. Current studies predominantly focused on autonomous scanning and imaging, with limited attention to downstream tasks such as disease diagnosis and analysis. Building on these observations, it identified critical challenges and future trends in RUSS development. This work provides a foundation for future research, fostering collaboration between clinicians and engineers to accelerate the translation of next-generation RUSS from bench to bedside.

Content generation modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and task-oriented generation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, molecular profile-morphology generation, and other specialized generation applications. By analyzing over 150 representative studies, we trace the evolution of content generation architectures-from early generative adversarial networks to recent advances in diffusion models and generative vision-language models. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing integrated and clinically deployable generation systems. This work aims to provide a foundational reference for researchers and practitioners developing content generation models in computational pathology.

Backed by a century of research and development, Hill-type models of skeletal muscle, often including a muscle-tendon complex and neuromechanical interface, are widely used for countless applications. Lacking recent comprehensive reviews, the field of Hill-type modeling is, however, dense and hard-to-explore, with detrimental consequences on innovation. Here we present the first systematic review of Hill-type muscle modeling. It aims to clarify the literature by detailing its contents and critically discussing the state-of-the-art by identifying the latest advances, current gaps, and potential future directions in Hill-type modeling. For this purpose, fifty-eight criteria-abiding Hill-type models were assessed according to a completeness evaluation, which identified the modelled muscle properties, and a modeling evaluation, which considered the level of validation and reusability of the models, as well as their modeling strategy and calibration. It is concluded that most models (1) do not significantly advance beyond historical foundational standards, (2) neglect the importance of parameter identification, (3) lack robust validation, and (4) are not reusable in other studies. Besides providing a convenient tool supported by extensive supplementary materials for navigating the literature, the results of this review highlight the need for global recommendations in Hill-type modeling to optimize inter-study consistency, knowledge transfer, and model reusability.

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.

Exploring brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, has demonstrated enormous potential in biomarker development and neuroscientific research. A range of techniques, from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from estimating brain responses to individual heartbeats to interactions linking the heart to changes in brain organization. This review provides an overview of the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of physiological disruptions on brain-heart interactions, solidifying it as a biomarker. The vast outlook of these methods could provide tools for disease stratification in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.

Over the past two decades, intravascular optical coherence tomography (IVOCT) has emerged as a promising tool for planning percutaneous coronary interventions (PCI), studying coronary artery disease, and assessing treatments. With its near-histological resolution and optical contrast, IVOCT uniquely evaluates coronary plaque characteristics, enhancing the guidance of interventional procedures. Artificial intelligence (AI) techniques have been widely applied to IVOCT imaging, providing fast and accurate automated interpretation. These techniques hold significant potential for both clinical and research purposes. Clinically, automated analysis offers comprehensive assessments of coronary plaques, leading to better treatment decisions during PCI. For research, automated interpretation of IVOCT opens new avenues to understand the pathophysiology of coronary atherosclerosis. However, these techniques face several limitations, including issues related to spatial resolution, challenges in manual assessments, and the additional time required for these analyses. This review covers recent advancements and applications of AI techniques and computational simulation methods in IVOCT image analysis, including vessel wall segmentation, plaque characterization, stent analysis, and their clinical applications. Furthermore, we discuss the potential of AI-enhanced IVOCT analysis to facilitate personalized decision-making, potentially improving short- and long-term patient outcomes.

Lung cancer is the leading cause of cancer-related mortality worldwide. In addition to localizing and segmenting lung nodules, a non-invasive risk assessment system can also help clinicians tailor treatment decisions in a timely manner, ultimately improving patient outcomes. Artificial intelligence (AI) technologies are increasingly being used in medical imaging to assess the risk of lung nodules, especially for malignancy classification. However, little research has been conducted on the assessment of other related risks. This work comprehensively reviews AI applications in lung nodule risk assessment, including malignancy diagnosis, pathological subtype assessment, metastasis risk evaluation, specific receptor expression identification, and disease progression tracking. It details common public databases used and state-of-the-art AI techniques, along with their benefits and challenges like data scarcity, generalizability, and interpretability. We anticipate that future research will tackle these issues, thereby increasing the improved interpretability and generalizability of AI methods in clinical workflows.

Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics-related tasks. Despite the transformative potential of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.

Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanismbased trials remain underutilized in neuroscience due to the brain's complexity. This situation may change through the use of a Virtual Brain Twin (VBT), which is a personalized digital replica of an individual's brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This Review outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, multiple sclerosis, and epilepsy. Finally, we discuss potential extensions to other neurological disorders, such as Parkinson's disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brainmachine integration.

Cardiac organoids represent an important bioengineering opportunity in the development of models to study human heart pathophysiology. By incorporating multiple cardiac cell types in three-dimensional culture and developmentally-guided biochemical signaling, cardiac organoids recapitulate numerous features of heart tissue. However, cardiac tissue also experiences a variety of mechanical forces as the heart develops and over the course of each contraction cycle. It is now clear that these forces impact cellular specification, phenotype, and function, and should be incorporated into the engineering of cardiac organoids in order to generate better models. In this review, we discuss strategies for engineering cardiac organoids and report the effects of organoid design on the function of cardiac cells. We then discuss the mechanical environment of the heart, including forces arising from tissue elasticity, contraction, blood flow, and stretch, and report on efforts to mimic these biophysical cues in cardiac organoids. Finally, we review emerging areas of cardiac organoid research, for the study of cardiac development, the formation of multi-organ models, and the simulation of the effects of spaceflight on cardiac tissue and consider how these investigations might benefit from the inclusion of mechanical cues.

Immunomechanobiology, the study of how physical forces influence the behavior and function of immune cells, is a rapidly growing area of research. It is becoming increasingly recognized that mechanical stimuli, such as fluid shear forces, are a critical determinant of immune cell regulation. In this review, we discuss the principles and significance of various mechanical forces present within the human body, with a focus on fluid shear flow and its impact on immune cell activation and function. Moreover, we discuss engineering approaches used to study immune cell mechanobiology, and their implications in health and diseases such as cancer, autoimmune disorders, and infectious disease.

Genomics plays an essential role in the early detection, classification, and targeted cancer therapy based on the analysis of precise alterations at the molecular level. Using the most reliable approach is essential for the exact interrogation and cross-examination of complex and multi-high-dimensional "Multi-omics" cancer genomics data. In recent years, deep learning has been successfully utilized to deal with large cancer genomics data and has the potential to transform predictive biology. This review aims to explore the recent advancements in the application of deep learning models in basic cancer omics research, including different methodologies for the interrogation of bulk cancer omics data and the importance of cross-platform data integration. The paper provides insights into advantages, limitations, potential for improvement, research gaps, future direction, and an in-depth comparison of the models currently used in the field of cancer genomics, highlighting the crucial need for collaboration and interdisciplinary research in the field.

The human ear has emerged as a bidirectional gateway to the brain's and body's signals. Recent advances in around-the-ear and in-ear sensors have enabled the assessment of biomarkers and physiomarkers derived from brain and cardiac activity using ear-electroencephalography (ear-EEG), photoplethysmography (ear-PPG), and chemical sensing of analytes from the ear, with ear-EEG having been taken beyond-the-lab to outer space. Parallel advances in non-invasive and minimally invasive brain stimulation techniques have leveraged the ear's access to two cranial nerves to modulate brain and body activity. The vestibulocochlear nerve stimulates the auditory cortex and limbic system with sound, while the auricular branch of the vagus nerve indirectly but significantly couples to the autonomic nervous system and cardiac output. Acoustic and current mode stimuli delivered using discreet and unobtrusive earables are an active area of research, aiming to make biofeedback and bioelectronic medicine deliverable outside of the clinic, with remote and continuous monitoring of therapeutic responsivity and long-term adaptation. Leveraging recent advances in ear-EEG, transcutaneous auricular vagus nerve stimulation (taVNS), and unobtrusive acoustic stimulation, we review accumulating evidence that combines their potential into an integrated earable platform for closed-loop multimodal sensing and neuromodulation, towards personalized and holistic therapies that are near, in- and around-the-ear.

Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized domains like medical imaging, which require in-depth expertise, these models face notable challenges arising from the medical field's inherent complexities and unique characteristics. In this review, we delve into the potential applications of AGI models in medical imaging and healthcare, with a primary focus on LLMs, Large Vision Models, and Large Multimodal Models. We provide a thorough overview of the key features and enabling techniques of LLMs and AGI, and further examine the roadmaps guiding the evolution and implementation of AGI models in the medical sector, summarizing their present applications, potentialities, and associated challenges. In addition, we highlight potential future research directions, offering a holistic view on upcoming ventures. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.

Genome and transcriptome engineering have emerged as powerful tools in modern biotechnology, driving advancements in precision medicine and novel therapeutics. In this review, we provide a comprehensive overview of the current methodologies, applications, and future directions in genome and transcriptome engineering. Through this, we aim to provide a guide for tool selection, critically analyzing the strengths, weaknesses, and best use cases of these tools to provide context on their suitability for various applications. We explore standard and recent developments in genome engineering, such as base editors and prime editing, and provide insight into tool selection for change of function (knockout, deletion, insertion, substitution) and change of expression (repression, activation) contexts. Advancements in transcriptome engineering are also explored, focusing on established technologies like antisense oligonucleotides (ASOs) and RNA interference (RNAi), as well as recent developments such as CRISPR-Cas13 and adenosine deaminases acting on RNA (ADAR). This review offers a comparison of different approaches to achieve similar biological goals, and consideration of high-throughput applications that enable the probing of a variety of targets. This review elucidates the transformative impact of genome and transcriptome engineering on biological research and clinical applications that will pave the way for future innovations in the field.

Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) models tailored to the intricacies of the medical field, bridging the gap between limited AI models and the varied nature of healthcare practices. The advancement of a healthcare foundation model (HFM) brings forth tremendous potential to augment intelligent healthcare services across a broad spectrum of scenarios. However, despite the imminent widespread deployment of HFMs, there is currently a lack of clear understanding regarding their operation in the healthcare field, their existing challenges, and their future trajectory. To answer these critical inquiries, we present a comprehensive and in-depth examination that delves into the landscape of HFMs. It begins with a comprehensive overview of HFMs, encompassing their methods, data, and applications, to provide a quick understanding of the current progress. Subsequently, it delves into a thorough exploration of the challenges associated with data, algorithms, and computing infrastructures in constructing and widely applying foundation models in healthcare. Furthermore, this survey identifies promising directions for future development in this field. We believe that this survey will enhance the community's understanding of the current progress of HFMs and serve as a valuable source of guidance for future advancements in this domain.