The role of large language models in induced pluripotent stem cell-derived cardiomyocytes research and clinical translation

Background: Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are redefining cardiovascular regenerative medicine, yet challenges in differentiation fidelity, functional maturation, and scalable production restrain their full clinical potential. Aim: This review evaluates the pioneering integration of large language models (LLMs)—including GPT-4, BioGPT, and BioMedLM—into iPSC-CM research and translational therapeutics, with a focus on advancing precision, efficiency, and patient-specific care. Methods: Structured searches across biomedical and artificial intelligence-focused databases were conducted to map how LLMs augment literature mining, experimental design, multi-omics integration, and clinical translation, including personalized therapy prediction and drug safety assessment. Results: LLMs demonstrably surpass traditional tools in identifying gene-phenotype links, refining clustered regularly interspaced short palindromic repeats-based differentiation protocols, and merging patient-level datasets with iPSC-CM outputs. Limitations include model interpretability, reproducibility across genetically diverse populations, and ethical considerations regarding data privacy and bias. Conclusion: Despite these barriers, early translational applications demonstrate that LLMs can accelerate hypothesis generation, optimize laboratory-to-clinic pipelines, and enable high-fidelity, patient-specific cardiomyocyte modeling. Relevance for patients: The synergy of LLM intelligence and iPSC-CM biology has the potential to deliver safer, more effective, and deeply personalized regenerative cardiac therapies—moving the field closer to truly bespoke heart repair.