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

Abstract

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.

Keywords

Artificial intelligence / Large language models / Biomedical natural language programs / Induced pluripotent stem cells / Cardiac regenerative medicine

Funding

None.

Conflict of interest

Tadahisa Sugiura is an Editorial Board Member of this journal and Guest Editor of this special issue, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

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