Abstract
This chapter explores the transformative potential of integrating knowledge engineering and artificial intelligence in healthcare, focusing on constructing a knowledge-based clinical decision support system (KBCDS). The primary objective is to design an AI-enabled health portal to enhance accessibility to medical advice globally. The study investigates the application of AI in diagnosing COVID-19 and pneumonia, aiming to improve diagnostic accuracy and speed, reducing the burden on healthcare systems, and saving lives. The research is based on extensive literature study, delving into the depths of knowledge engineering, AI applications in healthcare, and medical ontology. The findings underscore the transformative potential of the integrated approach, highlighting its impact on healthcare disparities globally. The AI-enabled health portal proves to be a reliable source of medical advice, demonstrating that the fusion of knowledge engineering and AI technologies empowers medical professionals and significantly enhances healthcare accessibility and diagnostic capabilities.
Top1. Introduction
In order to comprehend patients' illnesses, the cutting-edge healthcare support system (HSS) integrates technical hardware and software technologies (Shafiq et al., 2023). Diagnoses, treatments, monitoring, rehabilitation, clinical trials, epidemiology, and health education are just a few areas that have been transformed by the integration of AI in healthcare (Almotiri et al., 2023). Health telematics applications have profoundly affected all areas of medical care, including prevention, diagnosis, treatment, monitoring, rehabilitation, clinical trials, epidemiology, and health education (Zhao et al., 2023).
Research and development of artificial intelligence (AI) for healthcare has received active backing from the United States, the European Union, and the United Kingdom (Fatima et al., 2020). Intelligent systems that can make clinical decisions autonomously in healthcare are known as autonomous AI systems (Ghazal et al., 2021). To guarantee its advantages, autonomous AI must undergo a bioethical and responsible evaluation of its effects on patient outcomes, design, validation, data use, and responsibility (Festor et al., 2021).
Clinical decision support (CDS) systems provide healthcare providers, employees, and patients with important information and insights tailored to each individual's needs right at the point of treatment (Musen et al., 2021). These systems are vital parts of the healthcare support system. These systems improve the quality of treatment and decrease medical mistakes in various contexts, including reminders to maintain health, displays of pertinent information, screening for medication interactions, dosage modifications, order facilitators, workflow assistance, and more.
Knowledge engineering and AI, when combined, are causing a stir in the healthcare business at a time when unprecedented technical advancements are taking place. This research embarks on a visionary exploration, seeking to pioneer an integrated approach that seamlessly merges knowledge engineering and AI applications in healthcare. At the heart of this transformative endeavor lies the conceptualization and design of an Artificial Intelligence-enabled health portal—a Knowledge-Based Clinical Decision Support System (KBCDS) (Bashir et al., 2021). This innovative platform is meticulously designed to transcend geographical boundaries, offering enhanced accessibility to medical advice globally. The pressing need for a paradigm shift in healthcare delivery is the imperative driving this research. By focusing on developing an AI-driven health portal, we aim to address the challenges posed by the COVID-19 pandemic and pneumonia, specifically improving diagnostic accuracy and speed.
The overarching goal is to alleviate the strain on healthcare systems, thereby contributing to preserving precious human lives. Methodologically, this research stands on the solid foundation of an extensive literature study that delves into the intricate realms of knowledge engineering, AI applications in healthcare, and the nuanced domain of medical ontology. Rigorous analysis of existing studies and scholarly works has informed the conceptualization of our AI-enabled health portal and paved the way for a comprehensive understanding of the synergies between knowledge engineering and AI in healthcare (Khang & Hajimahmud et al., 2024).
The forthcoming chapters of this research unravel the intricacies of our integrated approach. We will navigate through the conceptual underpinnings of our Knowledge-Based Clinical Decision Support System, examining its potential to redefine healthcare accessibility and diagnostic capabilities. The research culminates in a profound exploration of the transformative impact of this integrated approach on healthcare disparities globally. As the reader embarks on this intellectual journey, our primary contention is that the fusion of knowledge engineering and AI technologies empowers medical professionals and catalyzes a paradigmatic shift in healthcare accessibility and diagnostic precision. This research, therefore, lays the groundwork for a future where healthcare transcends geographical constraints, bridging gaps and ensuring equitable access for diverse populations worldwide.
Key Terms in this Chapter
Knowledge Engineering: Knowledge engineers develop and execute strategies for gathering, organizing, and using information to address complex problems. The healthcare business relies on knowledge engineering to organize medical information, build decision support systems, and integrate knowledge-based technology like AI more easily into healthcare practices.
Medical Ontology: Medical ontology systematically represents medical concepts, entities, and relationships within a structured framework. It provides a standardized and formalized way to organize medical knowledge, enabling effective communication and sharing of information across different healthcare systems and applications. Medical ontology is integral to the development of semantic interoperability in
Transformative Healthcare: Bringing about significant and beneficial changes in healthcare delivery, accessibility, and outcomes is what we mean when we talk about transformative healthcare. All healthcare aspects, from treating individual patients to the whole system, may benefit from these innovative ideas and methods. Improved health outcomes, efficiency, and fairness in healthcare delivery are the goals of revolutionary healthcare.
AI Applications: AI applications are the practical use and implementation of artificial intelligence techniques, algorithms, and technologies to perform specific tasks, make decisions, or enhance processes across various domains. Diagnostic algorithms, decision support systems, and predictive analytics are some examples of AI applications in healthcare that aim to improve patient care.
Healthcare Disparities: When various demographic groups or populations have varied access to and experiences with healthcare, we say that healthcare disparities exist. Factors such as financial level, race/ethnicity, region, and cultural background may all shape these variations. Achieving universal healthcare access is a critical component in reducing healthcare inequities.