Generative AI - Assisted Adaptive Cancer Therapy
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
Adaptive combination therapy is deemed the most intuitive strategy to thwart therapeutic resistance through dynamic treatment tuning that accounts for cancer evolutionary dynamics. However, higher accuracy and reliability of treatment response predictions would be needed, in addition to the need for clinically feasible models of adaptive combination therapy that consider newly approved therapeutics and the growing multimodal data being available about cancer. Grounded in nonlinear system control theory, this review offers a perspective on exploiting GenAI learning and inferencing capabilities to predict treatment response and recommend treatments in the context of adaptive cancer therapy. Results from nonlinear system identification, control theory and deep learning are integrated within an adaptive cancer control framework to leverage the continuously expanding data about cancer and its treatment towards GenAI-enhanced adaptive therapy. The resulting models and their analysis contribute to a much-needed conceptual clarity about the research and translational pathways that would be needed to realize GenAI-assisted cancer treatments. In particular, they underscore that access to clinical data, deep learning opacity, and clinical validation present critical challenges that require adequate attention to pave the way towards acceptance and integration of GenAI in real-world oncology workflows.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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