From Data to Cure: Leveraging Artificial Intelligence and Big Data Analytics in Accelerating Disease Research and Treatment Development
Bibliographic record
Abstract
The emergence of novel genotyping, big data, data sharing, open-source algorithms, and powerful computing resources has facilitated the integration of artificial intelligence (AI) and big data to elucidate disease mechanisms, find implements for drug design and development, and enhance precision medicine. The parallel explosion of published studies containing large quantities of biomolecular data with accompanying clinical or pathology information has indeed benefited disease studies. However, similar to the biases that plagued many of the datasets used for the development of commercial AI algorithms, diagnosing radiological images, biological oddities or data imbalances could mislead the outcome of AI and big data studies. There is a high need of standardization and heterogeneous curation practices in the input data for AI and big data prometheus. Therefore, a systematic overview of the key technological advances, challenges, and emerging solutions for data mining is warranted for benchmarking and motivating further development of AI and big-data-guided disease research. As the window of opportunity for the rapid advance of artificial intelligence (AI) solutions to emergent challenges in drug discovery and global health narrows, there is an immediate need to harness big data, AI, and shared research infrastructure to optimize the performance of AI systems on small data sets in biomedicine. The coordination of such systems is a massive and complex engineering challenge that must safeguard researcher freedom while preventing misuse and enhancing collaboration. Effective governance of AI in the life sciences and biomedicine requires specifying requirements and mechanisms to monitor the evolution of each constructed system with respect to agreed-upon principles.
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
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 |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
| grok | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
| opus | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.008 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".