AI Powered Digital Transformation in Healthcare: Revolutionizing Patient Care through Intelligent and Adaptive Information Systems
Bibliographic record
Abstract
AI is leading a digital revolution of care, implementing cognizant and agile information systems while transforming interaction between patient and practice. Technologies like precision medicine, predictive analytics, and personalized healthcare solutions powered by AI streamline clinical workflows and enhance patient outcomes. Utilizing machine learning, natural language processing, and real time data analytics, healthcare systems can automate various administrative tasks, improve diagnostic precision and refine treatment protocols. Results from the analysis might provide insight into the impact of AI in healthcare delivery, particularly in areas of AI assisted imaging, virtual health assistants or predictive patient monitoring systems. AI holds enormous potential for tackling challenges, including resource limitations and increasing healthcare costs, but also presents ethical questions over data privacy, algorithm transparency and egalitarian access. The research underpins important developments, successful initiatives, early signs of adoption, and evolving trends, termed, and analyzed how AI enabled information systems are contributing to a new paradigm shift towards an efficient, integrated, patient focused healthcare ecosystem across the globe.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".