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By developing the technologies in tandem, personalized and predictive medicines become more attainable.
Artificial intelligence (AI) and machine learning (ML) are significantly advancing biomedical research, with applications in image analysis, drug discovery, and diagnostics. These technologies are also set to enhance organ-on-chip (OOC) platforms, which are 3D in vitro models used to study diseases and drug responses. OOC platforms generate complex, multi-dimensional datasets from real-time biosensors and high-content imaging, making them ideal for AI/ML analysis. However, the parallel development of these fields has not yet fully capitalized on their potential synergy. Researchers, led by Khurana et al., are working to establish a cohesive framework to integrate AI/ML with OOC platforms more effectively. This initiative aims to foster interdisciplinary collaboration across microfluidics, biology, computational modeling, and machine learning, while addressing key challenges such as data standardization and interpretability. The ultimate goal is to accelerate the development of more predictive and clinically relevant platforms for drug delivery, leading to improved understanding of drug responses and biological variability, and ultimately advancing personalized and predictive medicine.
This overview is based on the article titled “Intelligent organ-on-chip platforms: Machine learning in predictive and personalized drug delivery,” authored by Tanishq Khurana, Sourav Ganguly, and Kiran Raj M. The article was published in the scientific journal *Biomicrofluidics* in 2026.