Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities
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
The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it