Artificial Intelligence in Biology and Medicine

The McGowan Institute for Regenerative Medicine incorporates artificial intelligence (AI) to accelerate discoveries and improve applications in biology and medicine. AI tools process large datasets to identify patterns, design precise models, and support clinical decisions, enhancing efficiency in regenerative therapies.

Ongoing clinical and computational research employs novel AI and machine learning strategies in parallel studies of patients and large datasets. This work focuses on decoding complex biological signals—such as waveforms from the heart and brain—to enable early detection, prevention, and personalized treatment of conditions like sudden cardiac death, heart failure, and related regenerative challenges.

Recent publications, such as a 2025 roadmap in Advances in Wound Care co-authored by Drs. Sen and DeMazumder, outline practical applications of AI in health care and clinical research. These include core techniques like expert systems, machine learning, deep learning, and explainable AI, with examples in wound care, image analysis, predictive modeling for healing trajectories, multi-omics integration for biomarker discovery, and personalized treatment in regenerative contexts.

Although specific AI-focused programs are emerging, the institute applies these tools to personalize treatments, such as predicting wound healing based on patient data. This supports regenerative efforts by optimizing scaffold designs, gene analysis, and therapy combinations. Collaborations ensure AI integrates with electronic health records, wearable devices, and remote monitoring for real-time insights, while emphasizing transparency, ethics, rigor in reporting (e.g., checklists for authors and reviewers), and addressing challenges like data privacy and bias.