Bibliographic record
Abstract
The learning and inferencing capabilities of large language models (LLMs) that underlie generative artificial intelligence (GenAI) can be exploited to optimize cancer treatments and improve patient outcomes. The ability of these models to learn from large amounts of clinical, molecular, and radiomic data on cancer patients and their treatments is driving research interest in their application to treatment decision-making. The learning and predictive power of LLMs make them uniquely suitable for supporting adaptive cancer therapy. However, the clinical validation of GenAI support for clinical decisions in oncology needs to address the complexity and unique challenges of GenAI clinical interventions. The United States Food and Drug Administration (FDA) guidelines on the clinical evaluation of software as a medical device (SaMD) are explored as a basis for the clinical evaluation of GenAI-assisted adaptive cancer therapy. Metrics are proposed to address clinical associations and analytical validation along with an outlook on randomized clinical trials. This article provides a much-needed and timely perspective on the clinical evaluation of GenAI-assisted cancer treatments and provides insights into overcoming the inherent challenges of GenAI in its acceptance and adoption in real-world clinical settings.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".