Artificial Intelligence and Digital Neuromonitoring: Transforming Neurocritical and Neurohospitalist Care

Neurocritical care is at the forefront of a technological revolution driven by artificial intelligence (AI), machine learning (ML), and digital neuromonitoring. Modern neurocritical and neurohospitalist practices increasingly harness multimodal data streams, from continuous EEG and intracranial pressure (ICP) measurements to cerebral oxygenation, hemodynamic variables, and neuroimaging providing richer depictions of cerebral physiology than ever before. Yet, the volume and complexity of these high-resolution data challenge human interpretation and demand computational tools capable of uncovering patterns not discernible to clinicians. Recent advances in deep learning architectures such as convolutional, recurrent, and transformer-based networks have demonstrated remarkable performance in automated EEG interpretation and seizure detection. In parallel, predictive models for traumatic brain injury, stroke, and intracranial hypertension are reshaping prognostication paradigms, while digital health solutions are extending monitoring capabilities beyond the ICU. Despite this progress, barriers persist in data quality, external validation, interpretability, equity, and ethical oversight. The field now requires comprehensive frameworks that bridge technical innovation with clinical translation, patient safety, and fairness.This Research Topic aims to consolidate multidisciplinary insights into the design, validation, and clinical integration of AI and digital neuromonitoring systems in neurocritical and neurohospitalist practice. Specifically, it seeks to examine how computational models can enhance diagnostic precision, predict deterioration, and personalize treatments based on real-time, multimodal data. The Topic also invites exploration of tele-neurocritical care, wearable biosensors, and digital health platforms that extend neuro-monitoring capabilities across care settings. Furthermore, it aims to address equity, algorithmic bias, data governance, and ethics in the development and deployment of AI-assisted tools, ensuring inclusive and responsible implementation. Ultimately, this initiative intends to accelerate the convergence of neuroscience, data science, and precision medicine to improve neurological outcomes and optimize clinical workflows.The scope of this Research Topic spans the identification, integration, and clinical validation of AI-enabled and digital monitoring technologies across acute and post-acute neurological care, while focusing on translational, ethical, and implementation challenges. By uniting contributions from data scientists, clinicians, ethicists, and engineers, it encourages both methodological and applied research. To gather further insights in this evolving field, we welcome articles addressing, but not limited to, the following themes:• AI in neuromonitoring and signal interpretation, including automated EEG and ICP waveform analysis• Predictive analytics and prognostication across stroke, TBI, and disorders of consciousness• Multimodal data fusion and precision medicine frameworks integrating neuroimaging, electrophysiology, and clinical parameters• Digital health and remote monitoring innovations such as wearable biosensors, tele-neurocritical care, and mHealth tools• Implementation science, workflow integration, and human–machine teaming in neurocritical care• Algorithmic bias, model transparency, and governance frameworks ensuring fairness and ethical accountability• Validation, standardization, and reproducibility in clinical AI research, including multicenter and open-data approaches• Emerging frontiers including large language models, digital twins, and autonomous neurocritical systems Topic editors: Paraskevi Papadopoulou, Luk Arbuckle, Giuseppe Miceli, Pedro Elkind Velmovitsky Source: https://www.frontiersin.org/research-topics/80919/artificial-intelligence-and-digital-neuromonitoring-transforming-neurocritical-and-neurohospitalist-care