Big Data Analytics and Artificial Intelligence in Healthcare: Transforming Diagnostics, Treatment, and Disease Prevention.
Bibliographic record
Abstract
The integration of Big Data Analytics and Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, treatment, and disease prevention. This paper explores how these advanced technologies enhance clinical decision-making, improve patient outcomes, and optimize healthcare processes. By leveraging vast datasets, AI-driven algorithms facilitate early disease detection, predictive analytics, and personalized medicine, significantly reducing diagnostic errors and enabling timely interventions. Furthermore, machine learning models assist in tailoring treatment plans based on patient-specific data, leading to more effective and efficient therapeutic strategies. In disease prevention, big data analytics enable epidemiological surveillance, tracking disease patterns, and identifying at-risk populations. AI-powered predictive models support proactive interventions, reducing the burden of chronic illnesses and infectious diseases. The paper highlights key advancements in AI applications, including deep learning in medical imaging, natural language processing in electronic health records, and real-time analytics in wearable health devices. Despite these transformative benefits, challenges such as data privacy, ethical concerns, and integration complexities remain barriers to widespread adoption. The study concludes that while AI and big data analytics hold immense potential to reshape healthcare, addressing regulatory, infrastructural, and ethical considerations is crucial for sustainable implementation. By fostering interdisciplinary collaboration and robust policy frameworks, healthcare systems can harness these technologies to drive innovation, enhance efficiency, and improve global health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".