Abstract
Artificial intelligence (AI) has become invaluable in healthcare for disease diagnosis, treatment planning, and clinical decision-making. AI uses algorithms that emulate the human brain to learn, synthesize, analyze, generalize, and solve problems using natural language processing (NLP), machine learning, deep learning, and large language models. The accuracy and performance of AI algorithms depend on the quality and quantity of data they process. Maximizing the potential of AI algorithms requires feeding them substantial data from various healthcare systems. Unfortunately, most healthcare systems operate with different data formats and document architectures, compounding issues with unstructured, non-standardized data, which require increased storage space and processing time. Moreover, processing such data can introduce errors that may skew analysis results, highlighting the need for data interoperability in AI algorithms.
Herein, we examined the importance of interoperability, particularly semantic interoperability based on terminology standards, the global adoption of terminology standards, collaborative activities in terminology standard development, and proposed strategies to improve their usability in laboratory medicine.
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