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Artificial intelligence (AI) is revolutionizing microfluidics by enhancing design, automation, and data analysis. This integration promises significant advancements in drug discovery and personalized medicine.

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Area of Science:

  • Microfluidics and Artificial Intelligence (AI) integration.
  • Interdisciplinary applications of miniaturized fluidic systems.

Background:

  • Microfluidics offers advantages like low reagent consumption and faster kinetics due to reduced scale.
  • Miniaturization presents design and control challenges for complex applications.
  • Artificial intelligence (AI) is driving innovation across microfluidics, from design to data analysis.

Purpose of the Study:

  • To review key advances in microfluidics integrated with AI.
  • To discuss the potential and future outlook of combining AI and microfluidics.

Main Methods:

  • Leveraging AI, particularly neural networks, for numerical approximation of fluid dynamics (Navier-Stokes equations).
  • Utilizing machine learning for extracting patterns from large datasets generated by microfluidic automation.
  • Combining microfluidics with AI for precision control and automated data analysis.

Main Results:

  • AI enables accurate prediction of physicochemical properties through physics-informed neural networks.
  • Machine learning facilitates the discovery of complex patterns in microfluidic data.
  • AI integration streamlines microfluidic workflows, enhancing precision and automation.

Conclusions:

  • The synergy of AI and microfluidics, termed 'smart microfluidics,' holds transformative potential.
  • Future applications include high-throughput drug discovery, point-of-care diagnostics, and personalized medicine.
  • Continued integration of AI is key to unlocking the full capabilities of microfluidic systems.