Artificial intelligence and robotics are transforming how scientists grow iPS cells, the reprogrammed cells that could one day repair organs and tissues damaged by disease. Instead of relying on a researcher's experience and intuition, AI systems now automatically determine optimal conditions for cell growth, while robots execute those instructions with perfect consistency. This shift from manual craft to reproducible technology could accelerate the real-world adoption of regenerative medicine treatments.

What Are iPS Cells and Why Do They Matter?

Induced pluripotent stem cells, or iPS cells, are ordinary cells from skin or blood that have been reprogrammed by introducing a small number of genes, returning them to a state where they can transform into virtually any cell type in the body. Professor Yamanaka Shin'ya of Kyoto University first created iPS cells in mice in 2006, a breakthrough that opened the door to regenerative medicine as a treatment for diseases and injuries that cannot be prevented through lifestyle changes alone.

The challenge has always been scaling up production. Growing high-quality cells in large quantities requires maintaining precise conditions, temperature, nutrient delivery, and other variables. For decades, this work relied on the skill and experience of individual researchers, making it difficult to produce cells consistently and efficiently. That limitation is now changing.

How Is AI Automating Cell Cultivation?

In June 2022, researchers at Epistra, an AI startup based in Tokyo focused on life sciences, announced a breakthrough system that uses artificial intelligence and robotics to optimize iPS cell culturing. The system combines a robot called Mahoro with AI analysis software called Epistra Accelerate. The AI determines optimal conditions such as temperature and nutrient delivery, the robot cultures the cells based on those instructions, and the results are analyzed to determine the next set of conditions to test. By repeating this cycle, researchers have been able to efficiently produce high-quality cells.

"In the life sciences field, optimization approaches, also a core component of AI, have been playing an increasingly vital role in recent years. Conditions that once required significant time and effort to identify can now be determined more efficiently with AI," explained a researcher involved in the project.

Epistra Research Team, Life Sciences AI

The benefits of this automation are substantial. By combining AI and robotics, researchers can reduce variability in cell production processes, lower time requirements, and decrease costs. This technology is attracting significant attention as a means of promoting the social implementation of regenerative medicine derived from iPS cells.

Steps to Implement AI-Driven Cell Cultivation in Research

• Establish baseline data: Collect comprehensive data on current manual cell cultivation processes, including temperature ranges, nutrient compositions, timing, and quality outcomes across multiple batches to create a training dataset for AI systems.
• Select appropriate AI and robotic platforms: Evaluate systems like Epistra Accelerate and Mahoro that are specifically designed for life sciences applications and can integrate with existing laboratory infrastructure.
• Implement iterative optimization cycles: Use AI to test multiple condition combinations systematically, analyze results, and automatically adjust parameters for the next round of cultivation, reducing manual experimentation time.
• Monitor quality metrics continuously: Track cell viability, differentiation rates, and other quality indicators throughout the automated process to ensure consistency and identify any deviations from expected outcomes.

Why Does Personalized Medicine Depend on This Technology?

Among patients receiving the same diagnosis, one medication may be effective for some and ineffective for others. Similarly, some patients with early-stage cancer experience recurrence within a few years, while others diagnosed with terminal cancer survive for many years without recurrence. This variation has led researchers to question whether treatment decisions should rely solely on a doctor's experience.

Professor Kawakami Eiryo of Chiba University, who serves as team director of the Predictive Medicine Special Project at Riken, has pioneered a data-driven approach to medicine. His vision is to classify patients based on comprehensive data analysis and build a deeper body of knowledge to provide more effective, personalized care. AI-driven cell cultivation supports this goal by enabling researchers to create patient-specific cell models for drug testing and treatment development.

"I believe medicine that enables people to live long, stay healthy, and then die without prolonged illness is something that we can realistically achieve. Five or ten years won't be enough, but I hope that we will see this come to fruition by around 2050," said Professor Kawakami Eiryo.

Professor Kawakami Eiryo, Team Director of Predictive Medicine Special Project at Riken

How Are Organoids Advancing Drug Discovery?

Research on organoids, three-dimensional tissue cultures just a few millimeters in diameter that replicate part of the structure and function of a real organ, has been advancing significantly in recent years. Organoids are usually derived from iPS cells or similar cells and can reproduce the characteristics of human cells outside the body, making them invaluable for studying disease onset and progression.

A research team led by the Institute of Science Tokyo used iPS cells to create kidney organoids lacking the gene responsible for nephronophthisis, a government-designated intractable disease in Japan. By comparing these organoids with ordinary kidney cells, researchers discovered an abnormality in molecules involved in fibrosis, a process in which tissues become stiff. They also identified a medicine that prevents this abnormality, opening the way to new treatment methods.

Through combination with AI image analysis and omics data analysis, which comprehensively examines genes, proteins, and other molecular components, researchers are exploring potential advances in drug discovery and personalized medicine. Organoids and AI are now being increasingly integrated, creating a powerful platform for understanding disease and testing treatments tailored to individual patients.

What Does This Mean for the Future of Healthcare?

The convergence of AI, robotics, and regenerative medicine represents a fundamental shift in how medical treatments are developed and delivered. Instead of relying on broad population-level treatments, healthcare is moving toward precision medicine tailored to individual patient genetics and physiology. The automation of cell cultivation removes a major bottleneck in bringing these treatments from the laboratory to the clinic.

Wearable devices like smartwatches can now track heart rate, sleep, and other biometrics around the clock, and AI can analyze this stream of data to flag potential health risks at an early stage. For diseases and injuries that cannot be prevented through these preventive measures alone, regenerative medicine using iPS cells offers a powerful therapeutic option. As AI and robotics continue to improve the efficiency and consistency of cell production, these treatments are moving closer to becoming a practical reality for patients worldwide.