Bibliographic record
Abstract
Significant advances in the treatment of cancer have been achieved as reflected by the ever-expanding space of cancer therapeutics being available to cancer patients. Often, however, it is not clear which patient would respond to which drug and what combination of drugs will improve patient outcomes. Furthermore, while many of these drugs are initially effective, therapeutic resistance is often inevitable due to the evolving nature of cancer. Generative artificial intelligence (GenAI) powered by the increasingly large amount of accumulating clinical, molecular, and radiomics data about cancer patients and their treatments may serve as the kernel of rapid learning decision-support systems that could enable personalized cancer treatments to counter therapeutic resistance and overcome the shortcomings of the current standard of care. This perspective is explored in the context of current advances of AI applications in oncology and the potential of GenAI learning and inferencing capabilities to support patient-tailored dynamic cancer treatments. A discussion of this vision is elaborated with respect to issues pertinent to GenAI use in real-world clinical settings, including clinical validation, data curation, and sharing, large language model hallucinations as well as ethical concerns and considerations such as privacy, bias, transparency, and accountability.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".