Applications of artificial intelligence‐driven microfluidics in medical laboratory science
Qingtang Jiang, Qishun Mo, Chenchen Ge, Weihua Li, Jingwen Mai, Yao Chen, Ying Liu, Xiaoyan Deng, Zifeng Yang, Dou Wang
Abstract
Abstract Microfluidics has rapidly advanced in medical laboratory science (MLS) applications due to its precise detection capabilities, allowing for analysis with minimal sample volume and fast response time. However, traditional data processing methods in MLS face considerable challenges when extracting meaningful insights from large, complex datasets. Artificial intelligence (AI), with its advanced algorithms in machine learning, image processing, and pattern recognition, provides robust analytical tools that enhance data extraction and interpretation, advancing the development of intelligent microfluidics applications in MLS. This review presents the first comprehensive summary of AI‐driven microfluidics applications in MLS. Initially, the review introduces the basic concepts of AI and its advantages in data analysis. It then outlines the limitations of microfluidics, followed by a detailed discussion on the unique advantages of integrating AI with microfluidics. Next, the review presents various AI‐driven applications in microfluidic systems for detecting cells, bacteria, nucleic acids, proteins, emphasizing innovative methodologies in these areas. Finally, the review discusses current challenges and explores potential solutions of AI‐driven microfluidics for MLS.