Integrating artificial intelligence with droplet-based microfluidics: advances, challenges, and emerging opportunities
Integrating artificial intelligence with droplet-based microfluidics: advances, challenges, and emerging opportunities
Droplet-based microfluidics has revolutionized the lab-on-a-chip field by enabling precise generation and manipulation of monodisperse droplets that act as independent microreactors. Over two decades, innovations in passive geometries and active control methods have facilitated a wide range of droplet operations, driving applications in molecular diagnostics, single-cell analysis, drug discovery, and material synthesis. Despite these advances, challenges remain in reproducibility, scalability, and detection, alongside the growing need to manage complex experimental datasets. Parallel to these developments, artificial intelligence (AI) has evolved from early neural models to powerful deep learning and foundation architectures, offering transformative opportunities for droplet-based platforms. Supervised, unsupervised, and reinforcement learning approaches enhance droplet detection, sorting, and adaptive control, while deep learning architectures enable high-dimensional image analysis, time-dependent modeling, and multimodal data integration. Transfer learning and meta learning further address data scarcity, and emerging explainable AI frameworks provide interpretability critical for clinical and diagnostic applications. This review highlights the convergence of droplet-based microfluidics and AI, examining applications across droplet generation, detection, screening, and material synthesis and offering perspectives on challenges and future directions. Together, these fields promise to accelerate discovery and expand the clinical and industrial impact of microfluidics.
artificial intelligence, deep learning, droplet-based microfluidics, microfluidics, neural networks
Lai, Junyan
5ead0c35-add0-4b1a-9b17-251b65112ab1
Gaira, Sukrit
86804967-d568-446e-b1d9-d525b1eaeaf0
Wang, Rongfeng
34adc62a-f078-462a-ada6-041746b23cc8
Zhang, Qingtian
58f0e671-0e53-478f-8f4d-b94dd720893e
Li, Ming
734c0e4b-d284-491f-9cdc-ac394181bdf9
Wu, Liao
09de8a05-3807-4fec-b0eb-f3d2e1dda7a2
Li, Diangeng
261e1961-4a39-48fe-bfa6-df72bba96ce8
Li, Yi
2380bb30-5373-4a17-86c7-d51213480841
Zhang, Chengchen
abc47c06-4b99-4aed-be72-463f211e9dfa
Guo, Jinhong
d65d7044-32c8-4028-a6b3-d221ad8bf006
Tang, Shi Yang
1d0f15c6-2a3e-4bad-a3d8-fc267db93ed4
Lai, Junyan
5ead0c35-add0-4b1a-9b17-251b65112ab1
Gaira, Sukrit
86804967-d568-446e-b1d9-d525b1eaeaf0
Wang, Rongfeng
34adc62a-f078-462a-ada6-041746b23cc8
Zhang, Qingtian
58f0e671-0e53-478f-8f4d-b94dd720893e
Li, Ming
734c0e4b-d284-491f-9cdc-ac394181bdf9
Wu, Liao
09de8a05-3807-4fec-b0eb-f3d2e1dda7a2
Li, Diangeng
261e1961-4a39-48fe-bfa6-df72bba96ce8
Li, Yi
2380bb30-5373-4a17-86c7-d51213480841
Zhang, Chengchen
abc47c06-4b99-4aed-be72-463f211e9dfa
Guo, Jinhong
d65d7044-32c8-4028-a6b3-d221ad8bf006
Tang, Shi Yang
1d0f15c6-2a3e-4bad-a3d8-fc267db93ed4
Lai, Junyan, Gaira, Sukrit, Wang, Rongfeng, Zhang, Qingtian, Li, Ming, Wu, Liao, Li, Diangeng, Li, Yi, Zhang, Chengchen, Guo, Jinhong and Tang, Shi Yang
(2026)
Integrating artificial intelligence with droplet-based microfluidics: advances, challenges, and emerging opportunities.
Advanced Intelligent Systems, [e202501074].
(doi:10.1002/aisy.202501074).
Abstract
Droplet-based microfluidics has revolutionized the lab-on-a-chip field by enabling precise generation and manipulation of monodisperse droplets that act as independent microreactors. Over two decades, innovations in passive geometries and active control methods have facilitated a wide range of droplet operations, driving applications in molecular diagnostics, single-cell analysis, drug discovery, and material synthesis. Despite these advances, challenges remain in reproducibility, scalability, and detection, alongside the growing need to manage complex experimental datasets. Parallel to these developments, artificial intelligence (AI) has evolved from early neural models to powerful deep learning and foundation architectures, offering transformative opportunities for droplet-based platforms. Supervised, unsupervised, and reinforcement learning approaches enhance droplet detection, sorting, and adaptive control, while deep learning architectures enable high-dimensional image analysis, time-dependent modeling, and multimodal data integration. Transfer learning and meta learning further address data scarcity, and emerging explainable AI frameworks provide interpretability critical for clinical and diagnostic applications. This review highlights the convergence of droplet-based microfluidics and AI, examining applications across droplet generation, detection, screening, and material synthesis and offering perspectives on challenges and future directions. Together, these fields promise to accelerate discovery and expand the clinical and industrial impact of microfluidics.
Text
Advanced Intelligent Systems - 2026 - Lai - Integrating Artificial Intelligence With Droplet‐Based Microfluidics Advances
- Version of Record
More information
Accepted/In Press date: 19 January 2026
e-pub ahead of print date: 8 March 2026
Keywords:
artificial intelligence, deep learning, droplet-based microfluidics, microfluidics, neural networks
Identifiers
Local EPrints ID: 511509
URI: http://eprints.soton.ac.uk/id/eprint/511509
ISSN: 2640-4567
PURE UUID: 7a1b298d-649f-4148-a71d-cbf049e08feb
Catalogue record
Date deposited: 18 May 2026 16:50
Last modified: 19 May 2026 02:06
Export record
Altmetrics
Contributors
Author:
Junyan Lai
Author:
Sukrit Gaira
Author:
Rongfeng Wang
Author:
Qingtian Zhang
Author:
Ming Li
Author:
Liao Wu
Author:
Diangeng Li
Author:
Yi Li
Author:
Chengchen Zhang
Author:
Jinhong Guo
Author:
Shi Yang Tang
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics