Utilization of Ontology to Develop Artificial Intelligence Systems in the Healthcare Industry

Ontologies play a crucial role in healthcare systems due to the diversity of concepts, roles, users, and diagnostic and therapeutic methods. They facilitate the development of knowledge bases and the sharing and representation of information. With the integration of artificial intelligence (AI) into healthcare, ontologies can serve as complementary tools to enhance the quality of services.

This review study examines existing research on the application of ontologies in AI systems within the healthcare industry. By analyzing their applications, benefits, challenges, and limitations, the study seeks to provide a deeper understanding of their impact on advancing AI technologies and improving healthcare processes. In addition, the study offers recommendations for strengthening the development and use of ontologies in intelligent healthcare systems.

The findings of this review indicate that ontologies enhance the accuracy of results and support medical decision-making by enabling the semantic exchange of diverse and heterogeneous data. They are essential for the development of decision support systems and for fostering intelligent interactions between patients and healthcare systems. Furthermore, ontologies contribute to healthcare decision-making by semantically analyzing the connections between diseases, geographic regions, and environmental factors.

The use of ontologies in healthcare improves data analysis, patient diagnosis, treatment, and decision-making. Ontologies enhance data inference and interoperability in AI systems through data modeling, concept relationship extraction, knowledge enrichment, and information sharing. Given the vast scope of the healthcare domain, the diversity of specialties and data, and the absence of a dedicated ontology development methodology specific to this field, there is a clear need for a tailored and robust methodology.