Taxonomy of artificial intelligence and blockchain technology in telehealth systems
Bibliographic record
Abstract
Telehealth systems have rapidly emerged as a critical component of modern healthcare, enabling remote patient care, real-time monitoring, and improved access to medical services. With the advent of artificial intelligence (AI) and blockchain technology, telehealth systems have witnessed significant advancements in terms of efficiency, security, and interoperability. AI and blockchain technologies have emerged as powerful tools with immense potential in healthcare. However, there exists the problem of effectively categorizing and classifying the various applications and implementations of AI and blockchain technology in the context of telehealth systems. There is a need for a comprehensive taxonomy to organize and understand the diverse applications of AI and blockchain technology in telehealth due to the rapid growth in these fields. Therefore, this chapter presents a taxonomy that explores the integration of AI and blockchain in telehealth systems. The taxonomy aims to categorize, classify, and analyze the various applications, benefits, challenges, and future directions of this integration, providing a comprehensive understanding of the synergistic relationship among AI, blockchain, and telehealth. The chapter, thus, contributes to the advancement and adoption of these technologies in healthcare settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".