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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.