AI in flow cytometry: Current applications and future directions

Area of Science:

• Immunology and cellular biology
• Biotechnology and bioinformatics

Background:

• Flow cytometry is a crucial technique for analyzing cellular properties in research and diagnostics.
• Current limitations in flow cytometry include challenges in assay design and data analysis.

Purpose of the Study:

• To review current applications of artificial intelligence (AI) in flow cytometry.
• To explore future directions for AI integration in flow cytometry.

Main Methods:

• Literature review of AI applications in flow cytometry.
• Analysis of AI's impact on various aspects of flow cytometry workflows.

Main Results:

• AI is being applied to reagent selection, instrument standardization, panel design, data analysis, and quality control.
• AI demonstrates potential to significantly improve the efficiency and accuracy of flow cytometry.

Conclusions:

• AI integration offers transformative potential for flow cytometry across research, clinical trials, and diagnostics.
• Future AI development in flow cytometry will likely focus on automated assay design and advanced data interpretation.