Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 154 - 166
Опубликована: Янв. 1, 2024
Язык: Английский
Journal of Clinical Medicine, Год журнала: 2025, Номер 14(2), С. 550 - 550
Опубликована: Янв. 16, 2025
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding the brain, unlocking new possibilities in research, diagnosis, therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing by enabling analysis complex neural datasets, neuroimaging electrophysiology genomic profiling. These advancements are transforming early detection neurological disorders, enhancing brain–computer interfaces, driving personalized medicine, paving way for more precise adaptive treatments. Beyond applications, itself has inspired AI innovations, with architectures brain-like processes shaping advances algorithms explainable models. bidirectional exchange fueled breakthroughs such as dynamic connectivity mapping, real-time decoding, closed-loop systems that adaptively respond states. However, challenges persist, including issues data integration, ethical considerations, “black-box” nature many systems, underscoring need transparent, equitable, interdisciplinary approaches. By synthesizing latest identifying future opportunities, this charts a path forward integration neuroscience. From harnessing multimodal cognitive augmentation, fusion these fields not just brain science, it reimagining human potential. partnership promises where mysteries unlocked, offering unprecedented healthcare, technology, beyond.
Язык: Английский
Процитировано
8
Information, Год журнала: 2024, Номер 15(6), С. 325 - 325
Опубликована: Июнь 2, 2024
In the digital age, intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration this multifaceted relationship. This study aims to quantify characterize current research trends critically assess discourse on AI applications within HE. Employing mixed-methods design, we integrated quantitative data from Web Science, Scopus, Lens databases with qualitative insights selected studies perform scientometric content analyses, yielding nuanced landscape utilization in Our results identified vital areas through citation bursts, keyword co-occurrence, thematic clusters. We provided conceptual model for integration HE, encapsulating dichotomous perspectives AI’s role education. Three clusters were identified: frameworks policy development, academic integrity creation, student interaction AI. The concludes that, while offers substantial benefits educational advancement, it also brings challenges that necessitate vigilant governance uphold standards. implications extend policymakers, educators, developers, highlighting need guidelines, literacy, human-centered tools.
Язык: Английский
Процитировано
13
ACS Nano, Год журнала: 2024, Номер 18(34), С. 22620 - 22647
Опубликована: Авг. 1, 2024
Monitoring neurochemicals and imaging the molecular content of brain tissues
Язык: Английский
Процитировано
9
Journal of Asthma, Год журнала: 2025, Номер unknown, С. 1 - 9
Опубликована: Янв. 8, 2025
Integrating Artificial Intelligence (AI) into public health education represents a pivotal advancement in medical knowledge dissemination, particularly for chronic diseases such as asthma. This study assesses the accuracy and comprehensiveness of ChatGPT, conversational AI model, providing asthma-related information. Employing rigorous mixed-methods approach, healthcare professionals evaluated ChatGPT's responses to Asthma General Knowledge Questionnaire Adults (AGKQA), standardized instrument covering various topics. Responses were graded completeness analyzed using statistical tests assess reproducibility consistency. ChatGPT showed notable proficiency conveying asthma knowledge, with flawless success etiology pathophysiology categories substantial medication information (70%). However, limitations noted medication-related responses, where mixed (30%) highlights need further refinement capabilities ensure reliability critical areas education. Reproducibility analysis demonstrated consistent 100% rate across all categories, affirming delivering uniform Statistical analyses underscored stability reliability. These findings underscore promise valuable educational tool while emphasizing necessity ongoing improvements address observed limitations, regarding
Язык: Английский
Процитировано
1
The Innovation Life, Год журнала: 2024, Номер unknown, С. 100105 - 100105
Опубликована: Янв. 1, 2024
<p>Artificial intelligence has had a profound impact on life sciences. This review discusses the application, challenges, and future development directions of artificial in various branches sciences, including zoology, plant science, microbiology, biochemistry, molecular biology, cell developmental genetics, neuroscience, psychology, pharmacology, clinical medicine, biomaterials, ecology, environmental science. It elaborates important roles aspects such as behavior monitoring, population dynamic prediction, microorganism identification, disease detection. At same time, it points out challenges faced by application data quality, black-box problems, ethical concerns. The are prospected from technological innovation interdisciplinary cooperation. integration Bio-Technologies (BT) Information-Technologies (IT) will transform biomedical research into AI for Science paradigm.</p>
Язык: Английский
Процитировано
7
Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 138, С. 109369 - 109369
Опубликована: Сен. 27, 2024
Язык: Английский
Процитировано
5
Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 373 - 397
Опубликована: Ноя. 29, 2024
Язык: Английский
Процитировано
5
Imaging Neuroscience, Год журнала: 2024, Номер 2, С. 1 - 21
Опубликована: Апрель 23, 2024
Abstract To-date, brain decoding literature has focused on single-subject studies, that is, reconstructing stimuli presented to a subject under fMRI acquisition from the activity of same subject. The objective this study is introduce generalization technique enables subject’s based another subject, cross-subject decoding. To end, we also explore data alignment techniques. Data attempt register different subjects in common anatomical or functional space for further and more general analysis. We utilized Natural Scenes Dataset, comprehensive 7T experiment vision natural images. dataset contains multiple exposed 9,841 images, where 982 images have been viewed by all subjects. Our method involved training model one data, aligning new other space, testing second information aligned first compared techniques alignment, specifically ridge regression, hyper alignment. found possible, even with small subset dataset, specifically, using which are around 10% total namely performances comparable ones achieved Cross-subject still feasible half quarter number slightly lower performances. Ridge regression emerged as best fine-grained decoding, outperforming By subjects, high-quality potential reduction scan time 90%. This substantial decrease could open up unprecedented opportunities efficient execution advancements field, commonly requires prohibitive (20 hours) per
Язык: Английский
Процитировано
5
Brain Sciences, Год журнала: 2024, Номер 14(12), С. 1196 - 1196
Опубликована: Ноя. 27, 2024
Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements functional magnetic resonance imaging (fMRI) artificial intelligence (AI) have revolutionized the understanding management of this condition. This manuscript explores how integration these technologies has unveiled key insights into schizophrenia’s structural neural anomalies. fMRI research highlights disruptions crucial brain regions like prefrontal cortex hippocampus, alongside impaired connectivity within networks such as default mode network (DMN). These alterations correlate with cognitive deficits emotional dysregulation characteristic schizophrenia. AI techniques, including machine learning (ML) deep (DL), enhanced detection analysis patterns, surpassing traditional methods precision. Algorithms support vector machines (SVMs) Vision Transformers (ViTs) proven particularly effective identifying biomarkers aiding early diagnosis. Despite advancements, variability methodologies disorder’s heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, patient individuality must guide AI’s psychiatry. Looking ahead, AI-augmented holds promise tailoring personalized interventions, addressing unique dysfunctions, improving therapeutic outcomes individuals convergence neuroimaging computational innovation heralds transformative era precision
Язык: Английский
Процитировано
4
Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 33 - 64
Опубликована: Янв. 3, 2025
Advancements in artificial intelligence (AI) are revolutionizing neurophysiology, enhancing precision and efficiency assessing brain nervous system function. AI-driven neurophysiological assessment integrates machine learning, deep neural networks, advanced data analytics to process complex from electroencephalography, electromyography techniques. This technology enables earlier diagnosis of neurological disorders like epilepsy Alzheimer's by detecting subtle patterns that may be missed human analysis. AI also facilitates real-time monitoring predictive analytics, improving outcomes critical care neurorehabilitation. Challenges include ensuring quality, addressing ethical concerns, overcoming computational limits. The integration into neurophysiology offers a precise, scalable, accessible approach treating disorders. chapter discusses the methodologies, applications, future directions assessment, emphasizing its transformative impact clinical research fields.
Язык: Английский
Процитировано
0