Use of artificial intelligence in analytical systems for the clinical laboratory

Area of Science:

• Computer Science
• Artificial Intelligence
• Biomedical Informatics

Background:

• Information-processing technology has advanced analytical systems through standard computing software.
• Artificial intelligence (AI), encompassing expert systems and neural networks, represents a significant evolution in this domain.
• AI is defined by its capacity to handle incomplete, imprecise information and accumulate knowledge.

Purpose of the Study:

• To explore the role of software in system operation, control, and automation.
• To define artificial intelligence and its characteristics.
• To review AI applications in clinical laboratories.

Main Methods:

• Conceptual analysis of AI, expert systems, and neural networks.
• Review of AI applications in stand-alone and embedded systems.
• Discussion of AI's role in knowledge engineering and medical diagnosis.

Main Results:

• Expert systems rely on programmed knowledge, while neural networks learn through emulation of the human brain.
• AI applications span knowledge engineering, medical diagnosis, failure detection, image analysis, and natural language processing.
• The integration of neural network recognition with expert system rationalization shows future potential.

Conclusions:

• Artificial intelligence represents a collective intellectual property.
• There is a critical need for improved documentation, evaluation, and regulation of AI systems in clinical laboratories.
• AI's ability to manage complex data and learn offers transformative potential for analytical systems.