AI-Powered Blockchain Frameworks for Securing Healthcare Data against Emerging Cybersecurity Threats
Bibliographic record
Abstract
The rapid development of digital healthcare systems, such as Electronic Health Records (EHRs), Internet of Medical Things (IoMT) gadgets, and medical imaging platforms, has created chances for better patient care that have never been seen before. Current centralized security measures aren't always stable, adaptable, or open enough to protect private healthcare data. The paper presents an AI-enhanced blockchain system, a solution based on the decentralized ledger technology to identify and counter real-time threats. The security of healthcare data trades is due to the impossibility of data alteration and the guarantee of trust since the blockchain layer ensures security. The AI layer identifies bad behaviour and predicts bad things before they destroy the system. The proposed system enhances the privacy, integrity and availability of the data because of the tamper-resistant nature of blockchain and the versatile learning capabilities of AI. It can also be used to preemptively counter advanced hacks. The framework can attain reduced latency, enhanced detection and increased resistance to data manipulation than the conventional approaches. The design of the experiment and the methods of evaluation demonstrate this point. This project brings an addition of a non-invasive and effective means of securing healthcare facilities. It also provides us with valuable ideas on how to apply AI and blockchain in key sectors where information security is highly valued in the future.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".