Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103487 - 103487
Published: Nov. 1, 2024
Language: Английский
Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(2), P. 550 - 550
Published: Jan. 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.
Language: Английский
Citations
5
Frontiers in Cellular Neuroscience, Journal Year: 2025, Volume and Issue: 19
Published: Feb. 19, 2025
The brain's complex organization spans from molecular-level processes within neurons to large-scale networks, making it essential understand this multiscale structure uncover brain functions and address neurological disorders. Multiscale modeling has emerged as a transformative approach, integrating computational models, advanced imaging, big data bridge these levels of organization. This review explores the challenges opportunities in linking microscopic phenomena macroscopic functions, emphasizing methodologies driving progress field. It also highlights clinical potential including their role advancing artificial intelligence (AI) applications improving healthcare technologies. By examining current research proposing future directions for interdisciplinary collaboration, work demonstrates how can revolutionize both scientific understanding practice.
Language: Английский
Citations
2
Frontiers in Dental Medicine, Journal Year: 2024, Volume and Issue: 5
Published: Dec. 23, 2024
Artificial intelligence (AI) technology is being used in various fields and its use increasingly expanding dentistry. The key aspects of AI include machine learning (ML), deep (DL), neural networks (NNs). aim this review to present an overview AI, aspects, application biomedicine, dentistry, dental biomaterials focusing on restorative dentistry prosthodontics. AI-based systems can be a complementary tool diagnosis treatment planning, result prediction, patient-centered care. software detect restorations, prosthetic crowns, periodontal bone loss, root canal segmentation from the periapical radiographs. integration digital imaging, 3D printing provide more precise, durable, patient-oriented outcomes. also for automatic panoramic radiographs showing normal anatomy oral maxillofacial area. Recent advancement medical sciences includes multimodal fusion, speech data detection, neuromorphic computing. Hence, has helped dentists diagnosis, aid providing high-quality treatments less time.
Language: Английский
Citations
4
Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Journal Year: 2025, Volume and Issue: unknown, P. 33 - 64
Published: Jan. 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.
Language: Английский
Citations
0
Biophysical Reviews, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 15, 2025
Language: Английский
Citations
0
WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS, Journal Year: 2025, Volume and Issue: 24, P. 78 - 84
Published: March 31, 2025
This work presents an improved three-dimensional Hindmarsh-Rose neuron model that takes into account the impact of electromagnetic induction. By employing magnetic flux to characterize this influence, demonstrates how induction produces membrane potential through a feedback memristive current. For reason, simplest memristor model, which has been reported in literature, used. Interestingly proposed does not possess equilibrium points and can exhibit hidden coexisting firing patterns. Numerical simulations have conducted, unveiling system’s dynamics confirming exhibits
Language: Английский
Citations
0
Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103487 - 103487
Published: Nov. 1, 2024
Language: Английский
Citations
2