Bibliographic record
Abstract
Artificial intelligence (AI) is rapidly emerging as a transformative force in oncology, offering significant benefits across the cancer care continuum. Through advanced image analysis, AI enables earlier and more accurate diagnosis by detecting subtle abnormalities in radiology, pathology, and liquid biopsy data that may elude conventional assessment. Integrating multi-omics, clinical, and imaging datasets, AI supports highly personalized treatment planning, predicting therapeutic responses and guiding the selection of targeted agents and immunotherapies. Machine learning models also facilitate rapid drug discovery and repurposing, and improve patient access to clinical trials by matching tumor molecular profiles with trial eligibility criteria. In local therapies, AI enhances surgical navigation and radiotherapy planning, increasing precision while sparing healthy tissues. Continuous patient monitoring through wearable devices, electronic health records, and laboratory data allows AI systems to identify complications or recurrence earlier than standard follow-up methods. In supportive and palliative care, AI-driven tools anticipate side effects, optimize symptom management, and provide language, literacy, and psychological support. Furthermore, AI-enabled tele-oncology and translation services expand cancer care to underserved populations, addressing disparities in access. While ethical, regulatory, and technical challenges remain, the integration of AI into oncology holds immense promise for improving diagnostic accuracy, therapeutic efficacy, and quality of life for cancer patients worldwide.
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
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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".