Nonfungible Tokens in Cardiovascular Medicine
Bibliographic record
Abstract
The integration of nonfungible tokens (NFTs) in health care, particularly in cardiovascular medicine, represents a disruptive shift toward enhancing the security and interconnection of electronic health data around the patient. NFTs, unique digital certificates stored on a blockchain network, bind various sources of health data to their owner (the patient) and delineate the access rights for stakeholders (providers, researchers) using smart contracts. Data sources might include electronic medical records from different hospitals, clinics, pharmacies, test centres, and mHealth devices. Accordingly, patients and their providers benefit from seamless visibility of diagnoses, medications, electrocardiograms, imaging, home blood pressure logs, and artificial intelligence-enabled insights from these aggregated data. Rather than being stored on proprietary servers, data are encrypted and stored on decentralized networks with a unified point of access and immutable proof of ownership, making them more robust to theft or tampering. As custodians of their NFTs, patients are incentivized to actively partake in their health monitoring and self-driven research that aligns with their needs using innovative marketplaces that allow them to browse studies, document their informed consent, and monetize their contributions. Furthermore, they are empowered to educate themselves and seek care across siloes in traditional settings or virtual platforms such as the metaverse, where NFTs serve as digital passports. Despite these exciting prospects, adoption within the health care sector remains in its infancy, with ethical and technical limitations still being addressed. In this article we explore the multifaceted applications and key players in the field, and outline use-cases for patient-centred cardiovascular care featuring NFTs.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".