Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern healthcare should include artificial intelligence (AI) technologies for disease identification and monitoring, particularly for chronic conditions, including heart, diabetes, kidney, liver, and thyroid. According to the World Health Organization (WHO), heart, diabetes, and liver diseases (hepatitis B and C and liver cirrhosis) are leading causes of mortality. The prevalence of thyroid and chronic kidney diseases is also increasing. We conducted a comprehensive review of the available literature to assess the current state of AI advancement in disease diagnosis and identify areas needing further attention. Machine learning (ML), deep learning (DL), and ensemble learning (EL) approaches have gained popularity in recent years due to their excellent results across various medical domains. This study focuses on their application in disease diagnosis and monitoring. We present a framework designed to provide aspiring researchers with a foundational understanding of popular algorithms and their significance in disease identification. Additionally, we highlight the importance of blockchain technology in the healthcare industry for safeguarding patient data confidentiality and privacy. The decentralized and immutable nature of blockchain can enhance data security, promote interoperability, and empower patients to control their medical information. By demonstrating the potential of advanced ML methods and blockchain technology to transform healthcare systems and improve patient outcomes, our research contributes to the field of disease diagnostics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it