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Journal of Clinical and
Translational Research AI and LLMs in iPSC cardiac research

4. Conclusion Nonetheless, by framing both upstream and downstream
application nodes, this review offers a conceptual scaffold
4.1. Synthesizing the path forward to examine ethical, biological, and translational fault lines
LLMs have emerged not as passive computational tools but in LLM-guided regenerative medicine.
as cognitive collaborators in the evolution of cardiovascular Other important limitations remain, such as ethical
regenerative medicine. Their symbiosis with iPSC-CM risks (e.g., erroneous decisions made based on automated
technologies has redefined the possibility of bridging predictions), issues of bias in biological data, the need for
molecular depth with clinical foresight, transforming rigorous regulation and clinical validation, and institutional
static data into dynamic, patient-specific insight. From resistance to AI integration.
decoding transcriptomic vulnerabilities to simulating
drug responses and unmasking hidden cardiac signaling This review should be seen as both a map and a mirror—a
cascades, LLMs elevate regenerative cardiology from a reflection of current achievements and a roadmap for future
discipline of promise to a praxis of precision. empirical validation. Although fundamentally theoretical,
it is grounded in real-world implementations of LLMs
Through the lens of iPSC-derived cardiomyocytes,
LLMs do not merely predict outcomes—they actively and structured to highlight performance differentials,
practical gaps, and future benchmarks across AI platforms
co-shape them. Whether parsing the hidden linguistics of in cardiovascular regenerative medicine.
cardiac electrical signals or annotating the silent language
of mutated sarcomeric genes, these models act as translators 4.3. Future directions
between biology’s complexity and medicine’s intent.
Institutions in Japan, the United States, and others are The next frontier lies in the intentional integration of LLMs
already weaving LLMs into clinical pipelines, illustrating a with wet-lab protocols and clinical trials. This includes
future where human intuition and machine intelligence are dynamic LLM-based systems that adjust differentiation
harmoniously aligned in rhythm and resolution. protocols in real-time based on omics feedback from
iPSC-CM cultures, AI-assisted cryopreservation mapping
4.2. Limitations of this review to ensure graft integrity, or predictive frameworks for
While this review presents a comprehensive outlook on long-term graft-host interactions post-transplantation. In
the applications of LLMs in iPSC-CM research, it is not addition, LLMs must transition from being interpreters of
without limitations. First, the field is rapidly evolving, and known science to generators of new hypotheses, supporting
novel models—particularly multimodal foundation models regenerative surgeons and electrophysiologists in exploring
integrating text, images, and omics—emerge at a pace that novel frontiers of cardiac identity, within the constraints of
risks outdating current interpretations. The current analysis current interpretability and validation frameworks.
also leans heavily on literature and infrastructural models Globally, the emphasis must shift toward algorithmic
from HICs, which may not fully account for the logistical equity, particularly through the development of federated
and technological constraints present in LMICs. Challenges, and multilingual models so that iPSC-CM-based
such as limited access to high-throughput iPSC-CM therapeutics do not remain a privilege of academic elites
platforms, fragmented EHRs, and low local computational but a birthright of every human heart—regardless of
capacity, may hinder the real-world use of LLM-based geography, gross domestic product, or genetic background.
tools in these regions. While ethically aligned LLM A cross-continental commons for cardiac data, rooted in
deployment is emphasized, this review cannot substitute transparency and cultural humility, could democratize
for the legal, clinical, and sociotechnical audits necessary access and imagination.
before practical implementation in research or care. It does To catalyze translational acceleration, we propose a
not offer a validated benchmarking pipeline, nor does it modular evaluation framework that cross-references LLMs by
provide quantitative evaluations of model accuracy. Major output type (e.g., protocol optimization, variant annotation,
technical limitations include the lack of validation across
genetically diverse populations, insufficient quantification and predictive modeling), validation method (e.g., wet-lab
of uncertainties in LLM-driven predictions, and nascent cross-check and patient data alignment), and regulatory stage
regulatory frameworks for AI-based regenerative (research-only, preclinical, or investigational use) (Table 5).
therapies. These gaps impede reproducibility and hinder To summarize, LLMs are currently supporting
clinical translation, particularly in the context of LMICs. cardiovascular regenerative research by interpreting omics
Furthermore, the interpretability of transformer-based data, optimizing iPSC-CM differentiation protocols,
predictions remains a black-box challenge, demanding and simulating clinical outcomes. Their most immediate
post-hoc explainability layers before regulatory approval. promise lies in transforming static cardiac datasets into

Volume 11 Issue 5 (2025) 19 doi: 10.36922/JCTR025230026