382MO Enhancing phase I clinical trial selection using artificial intelligence: Evaluation of a large language model algorithm in a dedicated drug development unit
Bibliographic record
Abstract
Methods: Here, we present such a pipeline that can work with 3D surface scans from consumer-level devices, such as iPhones with LiDAR sensors.Our approach combines AI-based breast detection in 2D images with a back-projection method that maps segmentations onto 3D meshes, enabling accurate volume calculation.The system is currently being trained on more than 8,000 annotated images from the REQUITE dataset (www.requite.eu)to improve generalisability across diverse imaging conditions.Results: Validation on diverse 3D meshes demonstrates robust performance, opening up prospects for a clinical trial to evaluate accuracy on real scans.Importantly, the pipeline is fully automated, transparent in its intermediate steps, and designed to be adaptable to additional datasets, supporting continuous refinement. Conclusions:This pipeline is a first step toward objective and accessible breast volumetry.By removing reliance on specialised hardware, costly software, or "blackbox" algorithms, it lowers adoption barriers and empowers both patients and clinicians with bias-free measurements.Beyond being part of cosmetic evaluation, such open-source tools could guide reconstruction, enable longitudinal monitoring, and become a backbone for a model capable of predicting future appearance.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".