Get started
Work

Artificial intelligence and large language models in laboratory medicine: Applications and future perspectives

Year: 2026
Type: article
Abstract: Large artificial intelligence (AI) models are advancing rapidly across scientific and technological fields. AI-enabled big-data methods are also expanding their scope through large-scale data processing and analysis. In laboratory medicine, intelligent diagnostics based on AI integration of multi-omics and multimodal data are becoming an important direction of research. By combining different data types, investigators aim to achieve more precise disease diagnosis. Meanwhile, a new generation of diagnostic agents has emerged, such as sequential diagnostic orchestrators. In simulated cases, benchmark datasets, and controlled multispecialty evaluations, some systems have achieved diagnostic accuracy approaching or exceeding that of non-specialist physicians. These findings indicate proof-of-concept potential for coordinated decision support, but they do not establish autonomous end-to-end clinical readiness. Moreover, the central role of AI in laboratory medicine is not merely to interpret an abnormal value. It is to support the entire testing process across the pre-analytical, analytical, and post-analytical phases. Key applications therefore include multimodal data interpretation, discovery of new diagnostic indicators, laboratory report generation, optimization of personalized reference intervals, and development of diagnostic AI agents. Major challenges remain, including data standardization, privacy and security, model interpretability, clinical validation, and post-deployment monitoring. Research that aligns laboratory medicine with advances in AI will be an important priority over the next decade.
DOI: 10.1016/j.intdia.2026.100006
Language: English
Field: Medicine
Open Access status: hybrid
OpenAlex ID: W7215113648
FWCI:
Cites: 143
Cited by:
Related to: