Artificial Intelligence and Cloud-Enabled Big Data Analytics for Genomic Research: Transforming Healthcare Management Through RealTime Decision Support Systems and Predictive Modeling
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) and cloud-enabled big data analytics is revolutionizing genomic research, enabling real-time decision support systems and predictive modeling for advanced healthcare management. This study explores how AI-driven algorithms analyze vast genomic datasets to identify disease markers, predict patient outcomes, and personalize treatment strategies. Cloud computing provides scalable and secure infrastructure for processing large-scale genomic data, facilitating collaboration among researchers, clinicians, and healthcare institutions. Machine learning models enhance precision medicine by uncovering complex genetic patterns, improving diagnostic accuracy, and optimizing therapeutic interventions. Additionally, AI-powered predictive analytics supports early disease detection and population health monitoring, enabling proactive healthcare strategies. Key challenges, including data privacy, ethical considerations, and regulatory compliance, are examined alongside emerging solutions. This research highlights the transformative potential of AI and big data in genomic medicine, driving innovation in personalized healthcare and accelerating advancements in medical research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".