Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jain, Vishal; Mitra, Archan; Kumar, R. Ravi; Ravikumar, Aishwarya Title A name given to the resource Leveraging AI and Machine Learning for Healthcare Accessibility: Enhancing Clinical Decision Support Systems in Rural Africa Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Sustainable Healthcare Systems in Africa: Technologies, Practices, and Management;pp.286-297 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003530879-18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003530879-18</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020500452?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020500452?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jain V., Nitte Institute of Communication, Deemed to be University, India; Mitra A., Nitte Institute of Communication, Deemed to be University, India; Kumar R.R., Institute of Management &amp; Commerce, Srinivas University, India; Ravikumar A., MDS Oral and Maxillofacial SurgerySenior Resident, Government Dental College &amp; Hospital, India Description An account of the resource Healthcare in rural Africa is hindered by resource scarcity, limited infrastructure, and a shortage of trained professionals, contributing to high mortality and morbidity rates. This study examines the transformative potential of artificial intelligence (AI) and machine learning (ML) in clinical decision support systems (CDSS) to address these challenges. Focusing on diseases prevalent in the region, such as malaria, HIV/AIDS, and noncommunicable illnesses like diabetes, the research develops and evaluates AI-enhanced CDSS to improve diagnostic accuracy, treatment planning, and healthcare accessibility. This research contributes a framework for deploying AI-driven CDSS in resource-limited settings, with implications for enhancing global health outcomes. 2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104043747-6; 978-103287020-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online