Statement on the Effectiveness of AI and ML in Cancer Care
Bibliographic record
Abstract
With global cancer incidence and mortality rates continuing to rise, as projected by the World Health Organization, there is an urgent need for innovative approaches to cancer care. This chapter highlights the advances and challenges of integrating artificial intelligence and machine learning (AI/ML) in cancer screening, diagnostics, treatment and prognosis. AI/ML has enabled significant progress in early detection and diagnostic accuracy. AI models have accelerated drug discovery pipelines and support the development of personalized treatment strategies, especially for aggressive and complex malignancies. Moreover, AI/ML is increasingly deployed in predicting patient outcomes and improving quality of life through continuous monitoring and adaptive care. The chapter also critically addresses key considerations associated with clinical adoption, namely AI model bias, data privacy concerns, and the interpretability of complex models.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".