ArticleHealthcare informatics research2025
Utilization of Ontology to Develop Artificial Intelligence Systems in the Healthcare Industry.
Article in Healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-based predictive models for adverse blood donor reactions: a systematic review of immediate and delayed events and clinical data approaches.BMC medical informatics and decision making · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesOntologies 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.
methodsThis 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.
resultsThe 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.
conclusionsThe 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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.