AI-Based solutions for current challenges in regenerative medicine.

Area of Science:

• Regenerative Medicine
• Artificial Intelligence
• Biotechnology

Background:

• Regenerative medicine faces challenges in precision and efficiency.
• Artificial Intelligence (AI) offers advanced computational tools.
• Integrating AI can address key limitations in therapeutic development.

Purpose of the Study:

• To review AI applications in regenerative medicine.
• To examine AI's role in gene therapy, stem cell therapy, and tissue engineering.
• To highlight AI's potential for therapeutic innovation.

Main Methods:

• Review of AI techniques including machine learning and data fusion.
• Analysis of AI applications in gene therapy, cell therapy (e.g., mesenchymal stem cells), and tissue engineering.
• Examination of AI's impact on microscopy and biomaterial design.

Main Results:

• AI improves gene therapy accuracy and safety through genomic data analysis.
• AI enhances mesenchymal stem cell (MSC) characterization and potency prediction.
• AI optimizes tissue engineering by improving biomaterial design and predicting interactions.
• AI advances live cell culture analysis via enhanced microscopy.

Conclusions:

• AI integration significantly advances regenerative medicine.
• AI accelerates innovation in gene therapy, cell therapy, and tissue engineering.
• Interdisciplinary collaboration and AI adoption are crucial for future progress.