Page 21 - JCTR-11-5

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

data from underrepresented regions—including Japan, prediction frameworks show enormous conceptual
Indonesia, and countries in Latin America and Sub- promise, many remain preclinical or unpublished. The
Saharan Africa. 153-155 Only through such interdisciplinary, peer-reviewed literature currently offers limited prototypes.
decentralized collaboration can AI-enabled regenerative However, early signals are emerging. Japan’s Center for iPS
medicine evolve in a way that is not only innovative, but Cell Research and Application, for instance, has piloted
also just, safe, and globally relevant. closed-loop AI platforms that fine-tune cardiomyocyte
induction based on real-time metabolomic feedback. At
3.7. Future directions and global equity Stanford, reinforcement-learning algorithms are being
To unlock the full therapeutic scope of LLMs in trained to simulate post-graft electrical integration
cardiovascular regenerative medicine—particularly using iPSC-CM-derived bio-signatures. Bioreactor-
within iPSC-CM-based interventions—the next leap based cryopreservation mapping projects, aimed at
demands an infrastructure that is as globally inclusive as predicting graft viability and post-thaw functionality, are
it is scientifically robust. LMICs, such as Indonesia, other also in conceptual testing. While these efforts remain in
ASEAN members, and regions across Sub-Saharan Africa, development, their presence marks the beginning of a
remain underrepresented in both clinical trial participation tangible shift: from theoretical modeling to translational
and regenerative medicine access. To correct this, scalable pipelines.
LLM-driven systems must be embedded into public Hardware innovation must follow suit. Offline-
health frameworks where analog records, inconsistent compatible LLM interfaces and solar-powered diagnostic
connectivity, and resource constraints are the norm. By systems can mitigate bandwidth and electricity constraints
integrating mobile diagnostics, point-of-care telemetry, in remote settings. Open-source software, policy-aligned
and cloud-based EHR repositories, these systems can governance, and shared trial infrastructures—backed by AI
automate disease stratification, forecast trajectory shifts, consortia, such as OpenAI and Hangzhou DeepseekAI—
and personalize post-transplant management even in must underwrite this democratization.
decentralized care models.
In summary, the future of cardiovascular regenerative
The strategic development of federated learning medicine rests not only in molecular innovation or
ecosystems, in which anonymized cardiovascular datasets computational elegance—but in the shared will to heal.
from diverse regions are collaboratively trained without When LLMs are built, deployed, and trusted across every
breaching data sovereignty, ensures performance parity corner of the healthcare spectrum, they cease being tools
across ethnic, linguistic, and socioeconomic boundaries. of privilege and become instruments of equity. This is
Mobile LLM diagnostics, co-trained on electrophysiological the true legacy of an ethically coherent, biology-aligned,
data from iPSC-CM laboratories in Tokyo, Boston, and and human-centered cardiac future—where regenerative
emerging hubs, have begun enhancing arrhythmia and therapies reach every heart they are meant to save.
ischemia detection in rural clinics. Crucially, these systems
must be co-designed with local clinicians and patient 3.8. A comparative overview of LLMs in
communities to encode culturally relevant phenotypes and cardiovascular and regenerative contexts
avoid epistemic asymmetries—thereby maximizing trust, Despite the rapid proliferation of LLMs in biomedical
usability, and precision. research, few studies have conducted systematic,
Capacity-building remains essential. Regional training domain-specific evaluations of their performance across
pipelines for clinicians and technologists alike must match regenerative and cardiovascular contexts. This absence of
the deployment of AI-regenerative tools in LMICs. Tele- standardized benchmarking frameworks presents a notable
education modules, academic exchange programs, and gap in the translational landscape—particularly when
regional centers of excellence can catalyze local expertise considering the diverse architectural designs, training
and leadership. These efforts are beginning to materialize: corpora, and deployment pipelines that shape each model’s
academic-industry partnerships from Yogyakarta to clinical relevance.
Nairobi are already developing curriculum-integrated Emerging comparative studies have demonstrated
LLM training that supports both clinical interpretation that general-purpose LLMs, such as ChatGPT-4, excel
and translational research design. in contextualizing clinical guidelines and summarizing

Yet amidst this momentum, a measured realism literature with high fluency. However, they may
is necessary. While theoretical tools, such as real- underperform in multi-omics data integration due to a lack
time iPSC-CM protocol optimization, AI-assisted of domain-specific fine-tuning. In contrast, models such as
cryopreservation mapping, and graft-host compatibility BioGPT (Microsoft Research) and BioMedLM (Stanford

Volume 11 Issue 5 (2025) 15 doi: 10.36922/JCTR025230026