Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing)
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
In the realm of the emergence and spread of infectious diseases with pandemic potential throughout the history, plenty of pandemics (and epidemics), from the plague to AIDS (1981) and SARS (in 2003) to the bunch of COVID variants, have tormented mankind. Though plenty of technological innovations are overwhelmingly progressing to curb them—a significant number of such pandemics astounded the world, impacting billions of lives and posing uncovered challenges to healthcare organizations and clinical pathologists globally. In view of addressing these limitations, a critically exhaustive review is performed to signify the prospective role of technological advancements and highlight the implicit problems associated with rendering best quality lifesaving treatments to the patient community. The proposed review work is conducted in two parts. Part 1 is essentially focused upon discussion of advanced technologies akin to artificial intelligence, Big Data, block chain technology, open-source technology, cloud computing, etc. Research works governing applicability of these technologies in solving many uncovered healthcare issues prominently faced by doctors and surgeons in the fields of cardiology, medicine, neurology, orthopaedics, paediatrics, gynaecology, psychiatry, plastic surgery, etc., as well as their role in curtailing the spread of numerous infectious, pathological, neurotic maladies is thrown light off. Boundary conditions and implicitly associated challenges substantiated by remedies coupled with future directions are presented at the end.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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