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Dr. Shaik Balkhis Banu is an Assistant Professor in the Department of Physiotherapy at Fatima College of Health Sciences, Al Ain, United Arab Emirates. Her academic interests include healthcare and interdisciplinary applications of emerging technologies. She contributes to the discussion of ethical, trustworthy, and human-centred applications of Agentic AI.
The swift advancement of Artificial Intelligence (AI), machine learning, deep learning, generative AI, and autonomous intelligent systems is revolutionising healthcare from traditional reactive methods to predictive and individualised treatment models. Agentic Artificial Intelligence (Agentic AI) adds a new layer by allowing intelligent systems to comprehend patient-related data, analyse diverse healthcare information, establish goals, employ computational and clinical resources, and facilitate sequential decision-making with different degrees of independence. This section examines the utilisation of Agentic AI for forecasting human ailments and customising healthcare.The section additionally explores tailored healthcare, wherein autonomous systems might facilitate customised risk evaluation, preventive advice, treatment oversight, and patient involvement. Emphasis is placed on elucidating explainability, data privacy, cybersecurity, bias, clinical validation, interoperability, accountability, and human oversight. The chapter wraps up by addressing prospective avenues related to multimodal healthcare agents, digital health ecosystems, federated learning, edge intelligence, multimodal foundation models, and human-centric autonomous healthcare systems.
Remote Patient Monitoring (RPM) has become a crucial element of contemporary healthcare, facilitating the acquisition of patient health data beyond traditional clinical settings via wearable devices, Internet of Medical Things (IoMT) devices, mobile applications, interconnected medical apparatus, and remote sensing technologies. The amalgamation of Agentic Artificial Intelligence (Agentic AI) with RPM can revolutionise the collecting of health data from a passive approach to an intelligent, adaptable, and purpose-driven healthcare system. Agentic AI systems possess the capability to incessantly monitor physiological data, evaluate temporal health trends, identify any irregularities, access pertinent contextual information, synthesise insights from various data sources, and facilitate suitable healthcare processes. This section delineates the principles and framework of remote patient monitoring systems empowered by Agentic AI.The section underscores the potential of Agentic AI to provide proactive and tailored remote healthcare, while stressing the necessity of clinical supervision, safety, and ethical implementation.
Academics in tertiary education function within intricate professional settings that encompass instruction, research, administration, student engagement, assessment, publication obligations, and institutional duties. Such demands may lead to occupational stress and affect workplace wellness, productivity, involvement, and job contentment. Agentic Artificial Intelligence (Agentic AI) offers a novel methodology for the ongoing assessment of pertinent workplace metrics, recognising possible stress trends, forecasting alterations in well-being, and delivering tailored decision assistance. In contrast to traditional prediction models that mainly produce a stress score, agentic systems are capable of synthesising various analytical frameworks, contextual data, historical trends, and institutional assets to facilitate adaptive actions. A multi-agent framework encompassing workload, sentiment, well-being, recommendation, and institutional-support agents is examined. The significance of privacy, equity, transparency, consent, data management, cybersecurity, and human supervision is highlighted due to the involvement of sensitive personal data in workplace analytics. The presentation also includes assessment techniques, obstacles, and prospective pathways related to multimodal intelligence, federated learning, and individualised well-being agents.
Agentic Artificial Intelligence (AI) systems are progressively adept in sensing their surroundings, analysing intricate data, making choices, engaging with external systems, and executing tasks with differing levels of independence. These skills present considerable prospects in healthcare, education, finance, manufacturing, agriculture, cybersecurity, and various other fields, simultaneously posing issues concerning transparency, equity, accountability, privacy, safety, and human oversight. Clarifiable, principled, and reliable Agentic AI emphasises the necessity for autonomous systems to function in manners that are comprehensible, accountable, safe, and congruent with human principles and institutional standards. This section explores the clarity and openness in autonomous decision-making, ethical guidelines for self-governing systems, equity and bias reduction, safeguarding privacy, cybersecurity, safety, responsibility, and regulatory frameworks. It further examines techniques for assessing agent conduct, supervising autonomous choices, auditing artificial intelligence systems, and ensuring significant human oversight. Ultimately, new research avenues are investigated for creating dependable, ethical, and human-centric agentic AI systems that can facilitate autonomous decision-making while maintaining human agency and institutional responsibility.
This chapter explores the essential principles, system framework, communication technologies, sensor innovations, data-processing techniques, and real-world applications of IoT-driven smart healthcare. It showcases the amalgamation of edge computing, cloud computing, Artificial Intelligence (AI), and Machine Learning (ML) to facilitate anomaly detection, risk assessment, and individualised monitoring. The discourse encompasses applications in cardiovascular treatment, diabetes oversight, respiratory surveillance, geriatric care, post-surgical recuperation, and chronic illness management. The section also examines interoperability, data integrity, energy efficacy, network dependability, privacy, cybersecurity, patient authorisation, and the integration of healthcare workflows. When incorporated judiciously into healthcare services, IoT-enabled solutions can enhance continuity of care, enable timely identification of alarming changes, and augment access to specific monitoring services.
Mental health, psychological strain, and individual wellness have emerged as significant issues within educational establishments, professional environments, healthcare frameworks, and societal groups. Traditional mental health evaluation techniques frequently rely on self-reported surveys, therapeutic discussions, and intermittent assessments, which may fail to reflect ongoing fluctuations in a person's emotional and psychological condition. Artificial intelligence (AI) provides avenues for examining various data sources, recognising probable stress patterns, and facilitating early mental health evaluations via sophisticated predictive systems. Machine learning, deep learning, natural language processing, wearable sensing, and multimodal data analytics can be employed to analyse behavioural, physiological, environmental, and linguistic markers linked to stress and well-being. It additionally addresses explainable artificial intelligence, privacy-preserving methodologies, equity, human supervision, and ethical implementation. Ultimately, novel research trajectories such as individualised forecasting, federated learning, multimodal foundational models, and context-sensitive digital mental health frameworks are evaluated. The chapter establishes a thorough basis for creating dependable, ethical, and user-focused AI solutions that promote mental health awareness, early intervention, and overall well-being.
AI is revolutionising education with intelligent teaching systems, data-driven academic choices, learning analytics, and customised learning experiences. AI and educational technology can address these gaps through personalised material distribution, automated assessments, smart tutoring, student performance predicting, and early learning challenge identification. Artificial intelligence can help educators create exams, organise curricula, automate administrative duties, and identify educational gaps. AI-based education raises concerns about student privacy, algorithmic prejudice, digital disparities, data integrity, transparency, academic honesty, and overreliance on automated technologies. AI-enhanced education, learning analytics, and personalised learning principles, frameworks, algorithms, and limits are covered in this chapter. It proposes integrating educational data collection, predictive analytics, student profiling, adaptive teaching, and feedback. Proficient educational AI should help educators, develop student freedom, sustain human connection, and ensure fair access to meaningful learning activities.
Catastrophes resulting from natural phenomena and anthropogenic actions present considerable dangers to human existence, structural integrity, economic viability, and ecological balance. Traditional disaster management strategies frequently encounter obstacles such as sluggish information dissemination, restricted monitoring scope, disjointed communication, and insufficient forecasting abilities. IoT apparatus, such as environmental sensors, meteorological stations, seismic detectors, water-level gauges, drones, and interconnected infrastructure, facilitate ongoing data acquisition from disaster-affected regions. Mathematical models for forecasting hazards, evaluating risks, and optimising resources are provided to bolster the conceptual framework. The section additionally explores data dependability, model ambiguity, interoperability, privacy, cybersecurity, and ethical implications. Ultimately, novel research trajectories such as digital twins, federated learning, autonomous drones, satellite-driven analytics, and robust smart infrastructure are recognised. The section establishes a theoretical basis for creating intelligent, scalable, and robust disaster management systems.
The integration of autonomous AI, edge computing, and interconnected systems is revolutionising the advancement of future intelligent technology. Traditional computing frameworks frequently rely on centralised cloud systems, constant connectivity, and human-guided decision processes. This section explores the principles, structures, novel technologies, uses, obstacles, and prospective research avenues related to these advancements. A conceptual framework is introduced to demonstrate the amalgamation of autonomous decision-making, distributed intelligence, interconnected devices, and human supervision. Mathematical models for independent decision-making, edge work allocation, and system dependability are also examined. The chapter wraps up by highlighting research prospects in reliable autonomy, energy-efficient intelligence, adaptive networks, decentralised learning, and robust interconnected ecosystems.
Diabetes mellitus is a persistent metabolic condition marked by increased blood sugar levels and linked to severe complications such as cardiovascular ailments, renal impairment, neuropathy, and retinopathy. Prompt detection of persons predisposed to diabetes is crucial for prompt intervention, efficient disease management, and the avoidance of chronic consequences. Artificial intelligence (AI), especially through machine learning and deep learning, offers avenues to scrutinise diverse healthcare data and discern patterns linked to diabetes risk. This chapter explores AI-driven methodologies for forecasting diabetes and tailoring risk evaluations, encompassing data preprocessing, feature selection, predictive modelling, risk stratification, explainable AI, and clinical decision assistance. A comprehensive AI-driven system is suggested to evaluate diabetes risk by utilising personal health data and producing clear, individualised risk evaluations.Nonetheless, AI-generated forecasts ought to augment, rather than supplant, conventional diagnostic methods and expert medical discernment.
Cerebrovascular accidents represent a significant worldwide health issue and are a primary contributor to mortality and chronic impairment. ML provides avenues for examining clinical, demographic, laboratory, and lifestyle information to discern patterns linked to stroke risk. Techniques include logistic regression, decision trees, random forests, support vector machines, gradient boosting may be utilised for stroke prediction and risk assessment. This chapter offers an exhaustive analysis of stroke prediction utilising machine learning, addressing clinical risk factors, dataset selection, preprocessing, feature engineering, model construction, interpretability, evaluation metrics, and clinical decision support. The suggested approach establishes a basis for creating reproducible and clinically accountable stroke risk prediction systems. Such systems ought to enhance conventional clinical evaluations and require validation in suitable populations prior to clinical implementation.
Pneumonia and various respiratory ailments significantly contribute to global morbidity and mortality, necessitating prompt identification and precise diagnosis for optimal patient care. Chest X-ray imaging is extensively utilised to assess respiratory irregularities due to its accessibility, affordability, and frequent application in clinical settings. Nonetheless, the interpretation of chest radiographs can be difficult owing to the convergence of radiological patterns, discrepancies in picture quality, and resemblances between infected and noninfectious pulmonary diseases. This section offers an extensive summary of pneumonia and respiratory illness identification with deep learning techniques applied to X-ray imagery. It examines chest radiography, accessible datasets, picture preprocessing techniques, data augmentation methods, transfer learning, CNN architectures, attention mechanisms, transformer-based methodologies, multiclass classification, explainable AI, and model assessment. The section additionally explores issues related to dataset bias, class imbalance, data leakage, diagnostic ambiguity, interpretability, and clinical implementation.
Alzheimer's disease is a progressive neurological condition marked by a decline in memory, cognition, behaviour, and the capacity to carry out daily tasks. Prompt recognition of at-risk persons and thorough examination of cognitive deterioration are crucial for prompt clinical evaluation, suitable care strategising, and enhanced disease oversight. Machine learning (ML) methodologies provide avenues to examine these varied information sources and discern patterns linked to Alzheimer’s disease, mild cognitive impairment, and progressive alterations in cognitive performance. This chapter offers an extensive summary of machine learning-driven predictions for Alzheimer’s disease and the analysis of cognitive deterioration, encompassing clinical data, neuropsychological evaluations, magnetic resonance imaging (MRI), positron emission tomography (PET), genetic data, and longitudinal patient records. The section also explores difficulties related to data variability, absent data points, class disproportion, model transparency, privacy concerns, equity, and clinical verification. The suggested methodology highlights the need of reproducible experimentation and the conscientious incorporation into healthcare processes. Machine learning need to be viewed as an adjunct to clinical decision-making rather than a substitute for thorough medical assessment.
Parkinson's disease (PD) is a progressive neurodegenerative condition marked by motor symptoms such tremors, rigidity, bradykinesia, and postural instability, with non-motor issues including sleep problems, cognitive decline, depression, and autonomic dysfunction. Timely identification and accurate evaluation of disease advancement are crucial for facilitating clinical assessment, therapeutic strategy formulation, and ongoing patient surveillance. This section explores AI-based methodologies for identifying Parkinson’s disease, evaluating symptom intensity, and forecasting disease advancement. It offers a conceptual structure that amalgamates multimodal data preprocessing, feature extraction, feature selection, classification, longitudinal modelling, and interpretable AI. Supervised learning algorithms, ensemble techniques, convolutional neural networks, recurrent neural networks, and time-series models are examined concerning various data modalities. Evaluation methodologies for categorisation, calibration, clinical applicability, and longitudinal forecasting are likewise delineated. Ethical factors, safeguarding privacy, clarity of interpretation, and clinical verification are underscored. The suggested approach aims to facilitate research on AI-enhanced evaluation of Parkinson's disease, rather than supplant neurological assessments or expert medical opinion.
Machine learning (ML) is increasingly being investigated for predicting disease progression and treatment response using clinical records, laboratory measurements, medical imaging, physiological signals, genomic information, and longitudinal patient observations. Conventional healthcare assessment often relies on periodic clinical evaluations and population-level treatment guidelines, which may not fully capture individual differences in disease trajectories or therapeutic outcomes. It discusses supervised learning, ensemble algorithms, deep learning, survival analysis, time-series modelling, and causal inference approaches for different clinical prediction tasks. A conceptual framework is presented that integrates data acquisition, preprocessing, feature engineering, model development, longitudinal prediction, interpretability, and clinical validation. The chapter also explores applications in oncology, cardiovascular disease, diabetes, neurological disorders, and chronic inflammatory conditions.
Artificial Intelligence (AI) is progressively being investigated to enhance individualised therapeutic suggestions and treatment refinement by amalgamating patient-specific details, medical history, diagnostic results, treatment outcomes, and pertinent biomedical facts. Traditional therapy methodologies frequently depend on established clinical protocols and aggregate data, which may inadequately consider personal variations in disease traits, coexisting conditions, medication sensitivity, and treatment efficacy. This chapter delineates a conceptual framework for individualised therapeutic suggestions and treatment enhancement, encompassing data amalgamation, patient characterisation, treatment-response forecasting, recommendation formulation, optimisation methodologies, and clinical assessment. It further addresses issues related to data integrity, algorithmic prejudice, interpretability, confidentiality, security, and clinical validation. Its therapeutic efficacy must be confirmed via stringent validation, forward-looking assessment, and suitable regulatory supervision prior its implementation in medical environments.
The growing accessibility of electronic health records, laboratory data, medical imaging, and clinical examination information has facilitated the creation of advanced healthcare systems that can identify many diseases through comprehensive analysis. Traditional diagnostic methods frequently assess clinical data and medical imaging in isolation, which may hinder the recognition of intricate connections among patient traits, physiological markers, and visual irregularities. This section introduces a theoretical model for the identification of several diseases utilising clinical and medical imaging information, encompassing multimodal integration, illness classification, and interpretable predictions. Future investigations ought to emphasise clinically meaningful datasets, resilient multimodal learning approaches, and forward-looking assessments to ascertain if integrated AI systems might enhance diagnostic precision and facilitate secure clinical decision-making.
Artificial Intelligence (AI) has emerged as a significant area of investigation in healthcare, aiding in illness forecasting, diagnostic categorisation, risk evaluation, and clinical decision processes. Notwithstanding advancements in predictive modelling, the lack of transparency in intricate algorithms continues to pose a considerable obstacle to their implementation in clinical settings. XAI tackles these issues via feature attribution, interpretable models, counterfactual reasoning, visual explanation methods, uncertainty assessment, and human-centric decision-support systems. This chapter offers an extensive framework for creating transparent and dependable AI systems for illness forecasting, diagnostics, and clinical decision assistance. It underscores the necessity for systematic evaluation of explanations and the requirement for clinical utility to be confirmed via independent validation and prospective research prior to implementation.
The use of multimodal artificial intelligence (AI) into intelligent education is revolutionising traditional pedagogical methods through the facilitation of adaptive teaching, insightful evaluation, and customised learning experiences. Conventional educational technologies frequently depend on restricted data sources, such test scores, attendance logs, or interactions within learning management systems, which may insufficiently reflect the varied learning requirements, cognitive involvement, and behavioural tendencies of individual learners. This section explores the principles, frameworks, and approaches of multimodal AI for intelligent education and individualised learning. It examines natural language processing, computer vision, speech recognition, learning analytics, transformer architectures, multimodal integration, knowledge tracing, and adaptive recommendation systems. The section further examines issues with data privacy, algorithmic prejudice, accessibility, interpretability, computational demands, and the ethical utilisation of student data. The chapter culminates by suggesting a learner-centric multimodal AI framework that integrates academic achievement, learning behaviours, interaction tendencies, and educational inclinations to foster inclusive, adaptable, and efficient learning, while maintaining instructor supervision and student independence.
Student involvement and scholarly achievement are crucial metrics of educational efficacy, learning advancement, and student accomplishment. Conventional educational evaluation techniques predominantly depend on test scores, attendance logs, assignment marks, and instructor assessments, which may inadequately reflect the varied behavioural, cognitive, emotional, and environmental elements affecting student learning. This chapter explores the theoretical underpinnings, data types, computational frameworks, and forecasting techniques related to multimodal AI-driven analysis of student engagement and academic achievement. Recent advancements in educational foundational models, learning analytics, federated learning, and adaptive recommendation systems are regarded as promising avenues for research. The chapter culminates by suggesting a multimodal prediction model that amalgamates academic records, digital learning engagements, and student-produced learning evidence to facilitate prompt educational interventions, equitable learning prospects, and data-driven academic decision-making.