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eBook/Digital Version available from:
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Score: 66 |
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| Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders |
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ISBN: 978-1-03-285890-6,
336 pages,
Hard Cover
ISBN-10: 1-03-285890-7 |
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| Copyright: |
2026 |
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| Edition: |
1st |
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| Editor: |
Kumar, S. N.; Zafar, Sherin; Naaz, Sameena |
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Specialties:
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Neurology
, Medical Informatics |
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| Publisher: |
CRC Press/Taylor & Francis Group |
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| Series Title: |
Edge AI in Future Computing |
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| List Price: |
$160.00 |
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| Google: |
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At A Glance
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This book delves into the transformative potential of artificial intelligence (AI) and machine learning (ML) as game-changers in diagnosing and managing neurodisorder conditions. It covers a wide array of methodologies, algorithms, and applications in depth. Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders equips readers with a comprehensive understanding of how computational intelligence empowers healthcare professionals in the fight against neurodisorders. Through practical examples and clear explanations, it explores the diverse applications of these technologies, showcasing their ability to analyze complex medical data, identify subtle patterns, and contribute to the development of more accurate and efficient diagnostic tools. The authors delve into the exciting possibilities of AI-powered algorithms, exploring their ability to analyze various data sources like neuroimaging scans, genetic information, and cognitive assessments. They also examine the realm of ML for pattern recognition, enabling the identification of early disease markers and facilitating timely intervention. Finally, the authors also address the critical challenges of data privacy and security, emphasizing the need for robust ethical frameworks to safeguard sensitive patient information. This book aims to spark a conversation and foster collaboration among researchers, clinicians, and technologists, and will assist radiologists and neurologists in making precise diagnoses with enhanced accuracy.
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Reviewer:
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Karim Makhoul,
MD
(Northwell Health)
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Range
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Question
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Score
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| 1-10 |
Are the author's objectives met? |
6 |
| 1-10 |
Rate the worthiness of those objectives. |
8 |
| 1-5 |
Is this written at an appropriate level? |
5 |
| 1-5 |
Is there significant duplication? (1=significant, 5=insignificant) |
2 |
| 1-5 |
Are there significant omissions? (1=significant, 5=insignificant) |
3 |
| 1-5 |
Rate the authority of the authors. |
3 |
| 1-5 |
Are there sufficient illustrations? |
5 |
| 1-5 |
Rate the pedagogic value of the illustrations. |
2 |
| 1-5 |
Rate the print quality of the illustrations. |
2 |
| 1-5 |
Are there sufficient references? |
5 |
| 1-5 |
Rate the currency of the references. |
3 |
| 1-5 |
Rate the pertinence of the references. |
3 |
| 1-5 |
Rate the helpfulness of the index. |
3 |
| 1-5 |
If important in this specialty, rate the physical appearance of the book |
N/A |
| 1-10 |
Is this a worthwhile contribution to the field? |
6 |
| 1-10 |
If this is a 2nd or later edition, is this new edition needed? |
N/A |
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Reviewer:
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Karim Makhoul,
MD
(Northwell Health)
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Description
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| This book explores the integration of artificial intelligence techniques in the evaluation and management of neurological diseases. The text presents multiauthor chapters covering topics including neurodiagnostics, computational modeling of nervous networks, biomarker detection, and emerging therapeutic technologies. The book is organized as an edited volume contributed to primarily by researchers in engineering and computer science disciplines. |
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Purpose
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| The book aims to provide a comprehensive overview of how computational intelligence can support the diagnosis and understanding of neurological disorders. With the expansion of artificial intelligence in medicine, these objectives are worthy. The book succeeds in presenting a survey of computational methods and introduces emerging technology that may influence future neurology practice. However, the book does not always clearly establish the borders between research innovation and clinical applicability, which limits its translational impact. While the book technically meets its objectives, the absence of consistent clinical context prevents the work from fully realizing a practical potential for real-world bedside application. |
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Audience
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| The book appears primarily intended for researchers, graduate students, engineers, and technologists working in the field of artificial intelligence and neuroscience rather than clinical neurologists. While the book mentions medical professionals as part of the audience, technical language and theoretical discussions suggest that clinicians may find limited benefits. Readers seeking a survey of computational approaches to neurological disorders will likely find the book useful, but it may not fully meet the needs of practicing neurologists. Editors possess strong technical credentials. However, the relative lack of clinicians among authors contributes to reduced clinical depth. Significant duplication across chapters and omissions regarding clinical translational potential affect the book's utility for its intended audience. |
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Features
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| The book covers a wide range of topics related to computational intelligence in neurology, including neuroimaging techniques, machine learning models, brain-computer interfaces, and disease algorithms. Organized as a multi-author volume, it presents a highly ambitious theoretical discussion of artificial intelligence in neurology with diverse perspectives but suffers from fragmentation. The writing is clear from a technical standpoint, though clinical explanations are sometimes insufficient. One notable strength is the discussion of computational studies in disorders such as Parkinson's disease and Alzheimer's disease, particularly the exploration of gait biomarkers that demonstrate potential diagnostic relevance. Illustrations are appropriately present. However, some errors in correlation with clinical disorders emphasize the lack of a clinical eye among the authors. Neurological disorders are frequently grouped together in discussions of symptoms, treatments, and imaging rather than being analyzed individually, which might be the basis of algorithmic thumbprinting. The text also tends to emphasize theoretical neuron computational modeling without potential for clinical implications. Some chapters revisit historical background already discussed earlier. Despite these limitations, the book succeeds in highlighting certain roles of artificial intelligence in neurological research. |
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Assessment
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| Overall, this book represents an informative technical resource that introduces the expanding role of computational intelligence in neurology. Its greatest utility lies with researchers and engineers as the text emphasizes theoretical modeling. In contrast, Application of Artificial Intelligence in Neurological Disorders, Bhupathyraaj et al. (Springer, 2024) adopts a more disease focused perspective, framing artificial intelligence within diagnostic and therapeutic contexts that may feel more accessible to clinical readers. Future editions would benefit from stronger physician collaboration. I recommend this book mainly to graduate students, computational scientists, and researchers interested in emerging technologies, but less so to practicing neurologists seeking clinically actionable guidance. |
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