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Artificial intelligence in biomedical and modern healthcare informatics /

Artificial Intelligence in Biomedical and Modern Healthcare Informatics provides a deeper understanding of the current trends in AI and machine learning within healthcare diagnosis, its practical approach in healthcare, and gives insight into different wearable sensors and its device module to help...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Ansari, M. A. (Editor), Anand, R. S. (Editor), Tripathi, Pragati (Editor), Mehrotra, Rajat (Editor), Heyat, Md Belal Bin (Editor)
Format: eBook
Language:English
Published: London : Academic Press, [2025]
Subjects:
Artificial intelligence > Medical applications.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Artificial Intelligence in Biomedical and Modern Healthcare Informatics
  • Artificial Intelligence in Biomedical and Modern Healthcare Informatics
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Preface
  • Acknowledgments
  • 1
  • Evaluating the role of artificial intelligence in modern public health: A future-oriented analysis of medical s ...
  • 1. Introduction
  • 2. Health crises in India
  • 3. Artificial intelligence: Need to reflect public health
  • 3.1 Intentionality
  • 3.2 Intelligence
  • 3.3 Adaptability
  • 4. Encounters: Public health
  • 4.1 Major challenges
  • 4.1.1 Data privacy
  • 4.1.2 Ethical complications
  • 4.1.3 Data bias
  • 4.1.4 Challenges for social work profession
  • 5. Solutions: Social work perspective
  • 6. Summary and conclusion
  • References
  • Further reading
  • 2
  • Upshots of healthcare with AI
  • 1. Introduction
  • 1.1 Need of AI in healthcare
  • 1.2 Artificial intelligence
  • 1.3 Implementation of AI
  • 1.4 Machine learning
  • 1.5 Relationship between AI, ML, and DL
  • 1.6 AI effectiveness on various sectors
  • 2. AI-Supporting technologies
  • 2.1 AI in healthcare
  • 3. Conclusion
  • References
  • 3
  • Artificial intelligence and machine learning-assisted robotic surgery: Current trends and future scope
  • 1. Introduction
  • 2. The need for AI/ML in biomedical applications
  • 3. Types of biomedical applications for patient monitoring using AI
  • 4. Scope of surgical robots
  • 5. Artificial intelligence models for robotics surgery
  • 6. Challenges and future directions of robotics surgery
  • 7. Conclusion
  • References
  • 4
  • A deep perspective of blockchain applications in healthcare sector and Industry 4.0
  • 1. Introduction
  • 1.1 Blockchain concept
  • 1.2 Brief history of blockchain technology in Industry 4.0
  • 1.3 Blockchain types and consensus mechanism.
  • 1.4 Block chain characteristics
  • 1.4.1 Decentralization
  • 1.4.2 Transparency
  • 1.4.3 Autonomy
  • 1.4.4 Security
  • 1.4.5 Immutability
  • 1.4.6 Traceability
  • 1.4.7 Anonymity
  • 1.4.8 Democratized
  • 1.4.9 Integrity
  • 1.4.10 Programmability
  • 1.4.11 Fault tolerance
  • 1.5 Features of a unified blockchain and relevant challenges
  • 2. Blockchain applications and use cases
  • 2.1 Cryptocurrency
  • 2.1.1 Hyperledger
  • 3. Cryptocurrencies
  • 4. Blockchain and healthcare systems
  • 4.1 Healthcare blockchain and barriers or difficulties
  • 4.1.1 Inadequate expertise their deployment in the related fields
  • 4.1.2 Delayed adoption of paperless working
  • 4.1.3 The government enforcements or efforts for deployment
  • 4.1.4 Failureness in bringing out cost-effectiveness
  • 4.1.5 Inadequate personal space
  • 4.1.6 Motivational crisis
  • 4.1.7 The unacceptance of cryptocurrencies
  • 4.1.8 Inadequate security mechanisms
  • 4.1.9 Lack of unified healthcare system
  • 4.1.10 Slowness of information and communication technology can create life risks
  • 5. Proposed algorithms
  • 6. Conclusion
  • References
  • Further reading
  • 5
  • Analyzing the role of machine learning techniques in healthcare systems
  • 1. Introduction
  • 2. Related work
  • 3. Issues and challenges of ML
  • 3.1 Applications of machine learning in healthcare: Use cases
  • 3.1.1 Treatment, diagnosis, and prediction of mental illness
  • 3.2 Case study on prediction of cardiovascular diseases using machine learning
  • 4. Result and discussion
  • 5. Conclusion
  • References
  • 6
  • Artificial intelligence, machine learning and deep learning in biomedical fields: A prospect in improvising med ...
  • 1. Introduction
  • 2. Artificial intelligence (AI), machine learning (ML) and deep learning (DL) in biomedical research
  • 2.1 Drug discovery
  • 2.2 Disease diagnosis and prognosis.
  • 2.3 Electronic health records (EHRs)
  • 2.4 Personalized medicine
  • 2.5 Medical robotics
  • 2.6 Diseases identification and diagnosis
  • 2.7 Medical imaging
  • 2.8 Drug discovering &amp
  • manufacturing
  • 2.9 Personalized medical treatment
  • 2.10 Disease prediction
  • 3. Application of AI via modeling with large scale brain imaging data in cognitive brain disorders: A deep insight
  • 3.1 Network approach to analyzed brain imaging data
  • 3.2 Machine learning method for classification and analysis of brain imaging data
  • 3.3 Deep neural network (DNN)
  • 4. Machine learning in breast cancer screening
  • 5. Risk and ethical issues with artificial intelligence in biomedical sciences
  • 6. Conclusions and prospects
  • References
  • 7
  • Artificial intelligence in respiratory diseases with special insight through bioinformatics
  • 1. Introduction
  • 2. Bioinformatics: The modern AI in biology
  • 3. Respiratory diseases and current situations
  • 4. Contribution of AI and bioinformatics in respiratory disease care
  • 4.1 In pneumonia
  • 4.2 In RSV
  • 4.3 In asthma
  • 4.4 In COVID-19
  • 5. Future of AI
  • 6. Conclusions
  • References
  • 8
  • Electroencephalography (EEG) in epilepsy care: An introduction
  • 1. Overview
  • 2. Historical perspective of EEG
  • 3. Neuronal basis of EEG
  • 4. EEG and its implications in human brain
  • 4.1 Scalp versus intracranial EEG recordings
  • 4.2 Challenges in EEG signal processing
  • 5. EEG in tackling epilepsy
  • 5.1 Brief overview of epilepsy in India
  • 5.2 EEG in seizure detection
  • 5.3 EEG in seizure prediction
  • References
  • 9
  • A review on brain-computer interface and its applications
  • 1. Introduction
  • 2. Neuroimaging approaches
  • 2.1 Electroencephalography
  • 2.2 Magnetoencephalography
  • 2.3 Electrocorticography
  • 2.4 Functional magnetic resonance imaging
  • 2.5 Near-infrared spectroscopy.
  • 3. Control signals in BCIs
  • 3.1 Visual evoked potentials
  • 3.2 Slow cortical potentials
  • 3.3 P300 evoked potentials
  • 3.4 Sensorimotor rhythms (mu and beta rhythms)
  • 4. Types of BCIs
  • 5. Feature extraction and selection
  • 6. Classification algorithms
  • 7. Applications of BCIs
  • 8. Conclusions
  • References
  • 10
  • Recent trends in metabolomics, machine learning and artificial intelligence
  • 1. Introduction
  • 1.1 Basics of metabonomics
  • 2. Conceptual basis for metabonomics
  • 3. Metabonomics approach to disease diagnosis
  • 4. How it is used for cancer biomarker detection
  • 4.1 Metabonomics and cancer research
  • 5. Different metabolic profiling techniques
  • 6. Sample handling for robust data generation
  • 7. Pattern recognition techniques for metabolomics
  • 8. Self-organizing maps
  • 9. Websites and databases available online and offline
  • References
  • 11
  • A comprehensive review on state-of-the-art imagined speech decoding techniques using electroencephalography
  • 1. Introduction
  • 1.1 EEG-based speech imagery BCI system
  • 1.2 EEG signal acquisition
  • 1.3 Neural evidence of imagined speech
  • 1.4 Publicly available datasets
  • 2. Review based on signal processing techniques
  • 2.1 Preprocessing and artifact removal techniques
  • 2.2 Feature extraction and selection
  • 2.3 Classification methods
  • 2.3.1 Machine learning based
  • 2.3.2 Deep learning based
  • 2.3.3 Transfer learning method
  • 3. Review based on choice of parameters
  • 3.1 Choice of dominant frequency band
  • 3.2 Channel selection
  • 3.3 Choice of imagined prompt
  • 3.4 Mode of stimulus presentation
  • 3.5 Repeated trials of imagery
  • 4. Conclusion and future directions
  • References
  • Further reading
  • 12
  • Parkinson's disease diagnosis, treatment, and future scope: An epilogue
  • 1. Introduction
  • 2. The pathophysiology behind Parkinson's disease.
  • 3. The progression and stages of the Parkinson's disease
  • 3.1 First stage
  • 3.2 Second stage
  • 3.3 Third stage
  • 3.4 Fourth stage
  • 3.5 Fifth stage
  • 4. One disease with various symptoms
  • 4.1 Bradykinesia
  • 4.2 Freezing of gait
  • 4.3 Impaired posture and balance
  • 4.4 Tremors
  • 4.5 Speech and writing changes
  • 5. Recent trends in the treatment of Parkinson's disease
  • 5.1 Drug delivery systems
  • 5.2 Deep brain stimulation
  • 5.3 Nigra cell transplantation
  • 5.4 Walking stick and video games
  • 5.5 Diagnostic and clinical assessment devices
  • Acknowledgment
  • References
  • 13
  • Recent advances in removal of artifacts from EEG signal records
  • 1. Introduction
  • 2. Background
  • 2.1 Characteristics of EEG
  • 2.2 EEG artifacts and their types
  • 2.2.1 Ocular artifacts
  • 2.2.2 Muscle artifacts
  • 2.2.3 Cardiac artifacts
  • 2.2.4 Extrinsic artifacts
  • 3. Single artifacts elimination techniques
  • 3.1 Regression methods
  • 3.2 Wavelet transform
  • 3.3 Blind source separation
  • 3.3.1 Principal component analysis
  • 3.3.2 Independent component analysis
  • 3.3.3 Canonical correlation analysis
  • 3.3.4 Source imaging-based method
  • 3.4 Empirical mode decomposition
  • 3.5 Filtering methods
  • 3.5.1 Adaptive filtering
  • 3.5.2 Wiener filtering
  • 4. Hybrid methods
  • 4.1 Empirical mode decomposition
  • blind source separation
  • 4.2 Wavelet-blind source separation
  • 4.3 Blind source separation-support vector machine
  • 5. Comparative analysis
  • 6. Conclusions
  • References
  • 14
  • Computer-aided diagnosis in healthcare: Case study on lung cancer diagnosis
  • 1. Introduction
  • 2. Statistics on lung cancer
  • 3. Types of lung cancer
  • 4. Lung cancer staging
  • 5. Diagnosis of lung cancer
  • 5.1 Imaging test
  • 5.2 Sputum cytology
  • 5.3 Bronchoscopy and biopsy
  • 5.4 Needle biopsy
  • 6. Lung cancer treatment.