Artificial Intelligence (AI) has significant potential to transform neurology by enhancing the diagnosis and treatment of brain diseases. Current tools use AI for accurate epilepsy diagnosis, brain mapping, and surgical planning. AI can analyze large data volumes, explore biomarkers clinicians might miss, and improve diagnosis and treatment accuracy. Machine learning algorithms can process extensive data sets and patient information, developing accurate models for diagnosis and treatment. AI can identify unique, unbiased patterns in data, potentially discovering new biomarkers or intervention methods. These AI-driven systems are faster and more efficient than manual processes. Furthermore, AI can aid in developing new treatments for neurological disorders like Alzheimer's and Parkinson's and create personalized treatment plans based on individual neurological profiles. AI's capacity to detect complex data patterns paves the way for revolutionary advancements in diagnosing and treating brain diseases. Given ethical challenges will arise along the way.
Despite AI algorithms existing for many decades and significant advancements in processing power, computational/AI applications in medicine still need to catch up. This special issue aims to bridge that gap by focusing on several key objectives: 1) presenting cutting-edge research in AI applications within Neurology, thus informing our readership about the latest developments; 2) discussing current challenges and identifying existing gaps in the field; 3) exploring the roadmap, hurdles, and strategies to enhance AI's reach within neurology; and 4) predicting and preparing for the ethical challenges that will inevitably emerge with the application of such advanced technology. The goal of this special issue is to facilitate a comprehensive understanding of the status quo while also charting the path forward for the application of AI in neurology.
This Research Topic focuses on the role of Artificial Intelligence (AI) in neurology, covering both clinical and experimental research. It explores the potential of various AI algorithms in identifying new biomarkers and harnessing big data to enhance binary clinical decisions. This deep dive into AI and big data strategies aim to increase the precision, efficiency, and breadth of neurological diagnoses and treatments. Alongside this, the topic acknowledges the ethical implications of AI in medicine. An expert review of forecasted ethical risks related to AI application in neurology is provided, along with an examination of strategies from other medical fields for managing these risks effectively. The goal is to address these ethical challenges and equip the field of neurology with a holistic view of the future of AI applications.
Artificial Intelligence (AI) has significant potential to transform neurology by enhancing the diagnosis and treatment of brain diseases. Current tools use AI for accurate epilepsy diagnosis, brain mapping, and surgical planning. AI can analyze large data volumes, explore biomarkers clinicians might miss, and improve diagnosis and treatment accuracy. Machine learning algorithms can process extensive data sets and patient information, developing accurate models for diagnosis and treatment. AI can identify unique, unbiased patterns in data, potentially discovering new biomarkers or intervention methods. These AI-driven systems are faster and more efficient than manual processes. Furthermore, AI can aid in developing new treatments for neurological disorders like Alzheimer's and Parkinson's and create personalized treatment plans based on individual neurological profiles. AI's capacity to detect complex data patterns paves the way for revolutionary advancements in diagnosing and treating brain diseases. Given ethical challenges will arise along the way.
Despite AI algorithms existing for many decades and significant advancements in processing power, computational/AI applications in medicine still need to catch up. This special issue aims to bridge that gap by focusing on several key objectives: 1) presenting cutting-edge research in AI applications within Neurology, thus informing our readership about the latest developments; 2) discussing current challenges and identifying existing gaps in the field; 3) exploring the roadmap, hurdles, and strategies to enhance AI's reach within neurology; and 4) predicting and preparing for the ethical challenges that will inevitably emerge with the application of such advanced technology. The goal of this special issue is to facilitate a comprehensive understanding of the status quo while also charting the path forward for the application of AI in neurology.
This Research Topic focuses on the role of Artificial Intelligence (AI) in neurology, covering both clinical and experimental research. It explores the potential of various AI algorithms in identifying new biomarkers and harnessing big data to enhance binary clinical decisions. This deep dive into AI and big data strategies aim to increase the precision, efficiency, and breadth of neurological diagnoses and treatments. Alongside this, the topic acknowledges the ethical implications of AI in medicine. An expert review of forecasted ethical risks related to AI application in neurology is provided, along with an examination of strategies from other medical fields for managing these risks effectively. The goal is to address these ethical challenges and equip the field of neurology with a holistic view of the future of AI applications.