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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63782
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Masters of computer applications
Bapuji institute of engineering and technology Davangere
Masters of computer applications
Bapuji institute of engineering and technology Davangere
Masters of computer applications
Bapuji institute of engineering and technology Davangere
Generative Orchestration in Cardiac Diagnostics Cardiovascular pathologies continue to represent the primary force behind mortality rates globally, thereby accentuating the high degree of necessity for diagnostic methods that exceed traditional observational limits. “GENERATIVE AI FOR CARDIAC RISK ASSESSMENT” This set of clinical trials constitutes a paradigm shift concerning cardiovascular diagnostics by benefiting from generative architectures, thus helping to solve the Data Scarcity Bottleneck, as well as improving accuracy of risk stratification. The Problem Inertia and Data Imbalance Conventional heart disease diagnostic systems are likely to be affected by Informational Fragmentation, where ECGs, MRIs- telemetry, or EHRs can be considered and stored as distinct pieces of information Further, the creation of Predictive Models is also hampered by Class Imbalance, because it describes a lack of High fidelity data for rare occurrences of cardiac anomalies Everything above gives rise to the preference that the model has for the physiological states the system is in, which may lead it to ignore the initial states of other states as well pathological signals The Solution Multi-Modal Generative Synthesis Our system can be conceptualized as a Synthetic Augmentation Engine “Using generative models to synthesize valid high-resolution patient data”. This is not just a matter of copying the already available documents since it looks for the Biological Latent Space to generate various edge case scenarios to improve the training of downstream classifiers with diagnoses. It is able to process and combine several kinds of information, for instance ECG waves.
KEYWORDS
The Generative AI for Cardiac Risk Assessment project represents a revolutionary change in the approach to diagnosing heart disease. Transitioning from the episodic and manual evaluation approach to a Synthetic Intelligence-based approach provides the means of filling the gaps in the information that currently characterizes today's healthcare system. The ability to synthesize multimodal telemetry data—from high-definition images through to longitudinal Electronic Health Records—provides a solid basis for detecting the hidden subclinical pathological signs that cannot be detected by current heuristics alone. The Value Proposition and Social Impact This technology represents more than just a technological improvement. Its Inference Reliability aspect allows the integration of an "Explainable AI" ("XAI") component into the process, which encourages a "Human in the Loop" interaction. The feasibility analysis proves that the platform is both feasible and socially significant.
1. M. Ibrahim — Generative AI for synthetic data across multiple medical domains (2025). 2. R. Kumar — A comprehensive review of machine learning for heart disease (Frontiers in AI, 2025). 3. UCI Machine Learning Repository — Heart Disease Data (Cleveland, et al.). VC Pezoulas—Synthetic data generation methods in healthcare: A review (2024). 4. WHO — AI ethics and governance guidance for large multi-modal models (Jan 18, 2024). 5 . MH Tilala — Ethical Considerations in the Use of Artificial Intelligence in Healthcare (2024). 6. B. Meder — Artificial intelligence to improve cardiovascular population health (2025). 7. A. Mir — A novel approach for effective prediction of heart disease (2024). 8. MH Akpinar — A Systematic Review of Image- and Signal-Based Studies (GANs in healthcare) (2024). 9. CDC / public health articles on health equity and AI risks (2024).
Publication Date
5/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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