Advancements in load-bearing tissue repair increasingly demand biomaterials that not only support structural integrity but also interact dynamically with the physiological environment. This review examines the latest progress in smart biomaterials designed for skeletal reconstruction, with emphasis on mechanoresponsive scaffolds, bioactive composites, and integrated microsensors for real-time monitoring. We explore material formulations that enhance osseointegration, resist micromotion-induced loosening, and modulate inflammatory responses at the bone–implant interface. Additionally, we assess novel fabrication methods—such as additive manufacturing and gradient-based material deposition—for tailoring stiffness, porosity, and degradation profiles to match host biomechanics. Special attention is given to sensor-augmented platforms capable of detecting mechanical strain, biofilm formation, and early-stage implant failure. Together, these technologies promise a new class of bioresponsive, diagnostic-capable constructs that extend beyond static support to become active agents in regenerative healing and post-operative monitoring. This multidisciplinary review integrates insights from materials science, mechanobiology, and device engineering to inform the future of implantable systems in skeletal tissue repair.

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery.

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery.

Spine connectomics and neural integration have redefined the spinal cord as a dynamic computational hub, pivotal for sensorimotor coordination and adaptive motor control. This review synthesizes advancements from 2015 to 2025, highlighting how high-density electrophysiology, optogenetics, and brain-machine interfaces (BMIs), including Neuralink’s high-resolution neural arrays, elucidate spinal circuit architecture and function. We explore predictive coding and Bayesian integration mechanisms within spinal networks, revealing their role in processing multimodal sensory inputs and modulating motor outputs. Cutting-edge technologies, such as Neuropixels-Cord probes and 7 Tesla axis-resolved fMRI, enable laminar-specific mapping, while computational models uncover small-world network properties and hierarchical organization. Clinical applications include closed-loop epidural stimulation for spinal cord injury (SCI) rehabilitation, memristive sensors for prosthetic feedback, and personalized neuromodulation for chronic pain and scoliosis. Neuralink’s bidirectional BMIs demonstrate potential in restoring motor and sensory functions post-SCI, though challenges like electrode stability persist. Ethical considerations, including equitable access and neurodata privacy, are critical as these technologies advance. Future directions involve AI-driven discovery, multi-omics integration, and non-invasive neuromodulation to achieve comprehensive spinal connectomes, promising precision therapies for neurological disorders and enhanced musculoskeletal health.

Computer Aided Detection (CAD) and diagnostic technologies and their application have emerged in the last decade. CAD applications have concentrated on identification of the region of interest, for example nodules in the lung in the images, and analysis of the characteristics of objects. CAD technologies take advantages of computer systems as well as image processing and analysis techniques to overcome perceptual and interpretive errors caused by radiologists in the process of image observation and interpretation. This fact is inevitable in the context of medical practice since experience of image readers may vary and each observer (such as a radiologist) has a unique visual search on an image. Furthermore, there are always inter and intra observer variability for interpretation of an image. To overcome these difficulties, different CAD systems are developed for medical image analysis. This paper presents a systematic review on different CAD systems for lung cancer classification using computed tomography images.