Clinical chemistry and laboratory medicineDec 27, 2025

From Automated Systems to Intelligent Agents in Laboratory Medicine

Updated Mar 16, 2026

Abstract

Agentic artificial intelligence systems represent a major evolution in technology, enabling complex task execution and coordination.

These systems can invoke multiple tools and compose command chains, combining reasoning with deep research.
Agentic AI differs from traditional large language models by incorporating autonomy and orchestration.
Digital twins enhance this technology by creating dynamic virtual representations that learn and adapt continuously.
The implementation of agentic AI in laboratory medicine could improve efficiency and resource management.
There are ethical, interpretability, and governance challenges associated with the use of these systems.

Simplified

Agentic artificial intelligence (AI) systems are distinguished by their ability to invoke multiple tools, compose command chains, and combine chain-of-thought reasoning with deep research to execute complex tasks and take actions. This represents a major evolution beyond machine learning and large language models (LLM), toward systems capable of planning, executing, and coordinating complex workflows. In contrast to traditional LLMs, which primarily focus on generating and classifying information, agentic AI introduces elements of autonomy, reasoning, and orchestration, while digital twins extend this concept to dynamic virtual representations of patients and laboratory processes, capable of continuous learning, simulation and adaptation. This transition has profound implications for laboratory medicine, a field characterized by high data complexity, multi-omics integration, and stringent operational demands. At the same time, laboratories face growing expectations regarding efficiency, resource stewardship, and value-based healthcare delivery. This article explores both the opportunities and limitations of agentic AI in laboratory medicine, highlighting its potential to move beyond static automation toward autonomous, outcome-driven innovation. It also examines the ethical, interpretability, and governance considerations that must accompany its implementation.

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Abstract

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