Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders

Biomedicines, Год журнала: 2024, Номер 12(10), С. 2415 - 2415

Опубликована: Окт. 21, 2024

Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), Parkinson’s disease (PD) significantly affect global health, requiring accurate diagnosis long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, optimize rehabilitation through predictive analytics, robotic systems, brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI ML systems influence treatment in neurorehabilitation among neurological disorders. Materials Methods: Studies were identified from an online search of PubMed, Web Science, Scopus databases with a time range 2014 to 2024. has been registered on Open OSF (n) EH9PT. Results: Recent advancements are revolutionizing motor conditions SCI, PD, offering new opportunities personalized care improved outcomes. These technologies clinical assessments, therapy personalization, remote monitoring, providing more precise interventions better management. Conclusions: is neurorehabilitation, personalized, data-driven treatments that recovery Future efforts should focus large-scale validation, ethical considerations, expanding access advanced, home-based care.

Язык: Английский

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Neuroethics and AI ethics: a proposal for collaboration

BMC Neuroscience, Год журнала: 2024, Номер 25(1)

Опубликована: Авг. 29, 2024

The scientific relationship between neuroscience and artificial intelligence is generally acknowledged, the role that their long history of collaboration has played in advancing both fields often emphasized. Beyond important insights provided by collaborative development, AI raise a number ethical issues are explored neuroethics ethics. Neuroethics ethics have been gaining prominence last few decades, they typically carried out different research communities. However, considering evolving landscape AI-assisted neurotechnologies various conceptual practical intersections neuroscience-such as increasing application neuroscientific research, healthcare neurological mental diseases, use knowledge inspiration for AI-some scholars now calling these two domains. This article seeks to explore how can stimulate theoretical and, ideally, governance efforts. First, we offer some reasons reflection on innovations AI. Next, dimensions think could be enhanced cross-fertilization subfields We believe pace fusion development innovations, broad underspecified calls responsibility do not consider from will only partially successful promoting meaningful changes applications.

Язык: Английский

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Efficient Skin Lesion Detection using YOLOv9 Network

Journal Medical Informatics Technology, Год журнала: 2024, Номер unknown, С. 11 - 15

Опубликована: Март 31, 2024

Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance accurately identifying various types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, wart. We conducted comprehensive experiments curated dataset comprising 2721 training images, 288 validation 145 test images. The was trained evaluated based on standard metrics such as Precision, Recall, mean Average Precision (mAP). Our results indicate promising accuracy, with overall of 60.5%, Recall 86.0%, mAP 81.4%. Class-wise analysis reveals varying levels across different disease classes, highlighting model's proficiency detecting common conditions acne wart lesions. Furthermore, provide insights into potential challenges limitations, size class imbalance, discuss avenues future research to address these issues. study contributes advancement AI-driven solutions underscores efficacy network

Язык: Английский

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Modeling and analysis of a parallel robotic system for lower limb rehabilitation with predefined operational workspace

Mechanism and Machine Theory, Год журнала: 2024, Номер 198, С. 105674 - 105674

Опубликована: Май 11, 2024

The paper presents the mathematical modeling and analysis of LegUp, a novel parallel robotic system for lower limb rehabilitation bedridden patients. operational workspace is defined based on set parameters that describe motion joints, which natural in task. To comply with this representation workspace, robot kinematic models dependency between actuators joints. Furthermore, singularity achieved joint space, shows whether singularity-free. achieve feasible mechanical design prescribed to ensure safe operation, are determined multi-objective optimization problem. Numerical simulations show singularity-free selected parameters, then used construct experimental model LegUp.

Язык: Английский

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Education and training in neurology: developments and future challenges

European Journal of Neurology, Год журнала: 2024, Номер 31(11)

Опубликована: Май 21, 2024

Training and education is essential for best practice medicine especially important in a rapidly evolving field such as neurology. Due to improved imaging techniques laboratory testing, there better understanding of the pathophysiology diseases. As result more treatments have become available. The most developments neurology over last two decades their effect on training are described. In addition, how future should be aware challenges ahead us

Язык: Английский

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Reviewing the Horizon: The Future of Extended Reality and Artificial Intelligence in Neurorehabilitation for Brain Injury Recovery

Information, Год журнала: 2024, Номер 15(8), С. 501 - 501

Опубликована: Авг. 21, 2024

People with disorders of consciousness, either as a consequence an acquired brain injury or traumatic injury, may pose serious challenges to medical and/or rehabilitative centers increased burden on caregivers and families. The objectives this study were follows: explore the use extended reality critical means support in people consciousness injuries; evaluate its impact recovery processes; assess improvements participants’ quality life, reduce families by using artificial-intelligence-based programs. A selective review newest empirical studies interventions patients injuries was conducted over last decade. potential for bias is acknowledged. conceptual framework detailed. data showed that programs successfully enhanced adaptive responding participants involved, improved their life. reduced accordingly. Extended artificial intelligence be viewed crucial injuries.

Язык: Английский

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Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors

Frontiers in Radiology, Год журнала: 2025, Номер 4

Опубликована: Янв. 13, 2025

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and monitor the tumor's response. Recent AI advances in neuroimaging have promising applications including guiding clinical decisions improving patient management. However, lack of clarity on how arrives at predictions has hindered its translation. Explainable (XAI) methods aim improve trustworthiness informativeness, but their success depends considering end-users' (clinicians') specific context preferences. User-Centered Design (UCD) prioritizes user needs an iterative design process, involving users throughout, providing opportunity XAI systems tailored neuro-oncology. This review focuses intersection interpretation neuro-oncology management, explainable decision support, user-centered design. We provide a resource that organizes necessary concepts, evaluation, translation, experience efficiency enhancement, improved outcomes discuss importance multi-disciplinary skills creating successful systems. also tools, embedded human-centered decision-making process different from fully automated solutions, can potentially enhance clinician performance. Following UCD principles build trust, minimize errors bias, create adaptable software promise meeting expectations healthcare professionals.

Язык: Английский

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Venture Capital Investment in Neurology Companies

JAMA Neurology, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

This cross-sectional study categorizes and quantifies temporal thematic trends in investment neurology from 2000 to 2023.

Язык: Английский

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Embracing the changes and challenges with modern early drug discovery

Expert Opinion on Drug Discovery, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs discovered. As traditional faces growing challenges terms time, cost, efficacy, there a pressing need to integrate these emerging technologies enhance process. In this perspective, authors explore role AI ML modern discuss their application target identification, compound screening, biomarker discovery. This article based on thorough literature search using PubMed database identify relevant studies that highlight use AI/ML models computational chemistry, systems biology, data-driven approaches development. Emphasis placed how address key such as data integration, predictive performance, cost-efficiency pipeline. have potential revolutionize improving accuracy speed identifying viable candidates. However, successful integration requires overcoming related quality, model interpretability, for interdisciplinary collaboration.

Язык: Английский

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Artificial Intelligence‐Based Virtual Assistant for the Diagnostic Approach of Chronic Ataxias

Movement Disorders, Год журнала: 2025, Номер unknown

Опубликована: Март 22, 2025

Abstract Background Chronic ataxias, a complex group of over 300 diseases, pose significant diagnostic challenges because their clinical and genetic heterogeneity. Here, we propose that artificial intelligence (AI) can aid in the identification understanding these disorders through utilization smart virtual assistant. Objectives The aim is to develop validate an AI‐powered assistant for diagnosing chronic ataxias. Methods A non‐commercial was developed using advanced algorithms, decision trees, large language models. In validation process, 453 cases from literature were selected 151 causes ataxia. accuracy compared with 21 neurologists specializing movement GPT‐4. Usability regarding time number questions needed also evaluated. Results 90.9%, higher than (18.3%), GPT‐4 (19.4%). It significantly outperformed ataxia distributed by age, inheritance, frequency, associated manifestations, treatment availability. Neurologists mentioned 110 incorrect diagnoses, 83.6% which made GPT‐4, generated seven data hallucinations. required average 14 1.5 minutes generate list differential faster (mean, 19.4 minutes). Conclusions proved be accurate easy fast‐use diagnosis potentially serving as support tool neurological consultation. This approach could expanded other non‐neurological diseases. © 2025 International Parkinson Movement Disorder Society.

Язык: Английский

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