Explainable AI-driven precision clinical trial enrichment: demonstration of the NetraAI platform with a phase II depression trial
Bibliographic record
Abstract
Clinical trial failures are frequently driven by patient heterogeneity and limited sample sizes that obscure treatment effects by diluting statistical power. We introduce NetraAI, a novel explainable artificial intelligence (AI) platform that integrates dynamical-systems modeling, evolutionary long-range memory feature selection, and large-language model (LLM)-generated insights, to discover high-effect-size patient subpopulations ("Personas") from high-dimensional clinical data. In a Phase II ketamine trial for treatment-resistant depression (n = 63), NetraAI analyzed psychiatric scale data (175/patient) and MRI-derived features (185/patient). NetraAI outperformed traditional machine learning (ML) models in predicting treatment outcomes, improving predictive accuracy by approximately 25-30% and achieving higher sensitivity and specificity in detecting responders. NetraAI identified a 10-clinical variable model that improved predictive AUC by 0.32 over standard machine learning (ML) models and an 8-MRI feature model achieving 95% accuracy and 100% specificity. These findings demonstrate that an explainable dynamical AI approach can leverage small but rich datasets to uncover hidden clinically meaningful subgroups. NetraAI's precision enrichment strategy has the potential to improve trial success rates and enable personalized medicine by prospectively identifying patients most likely to benefit from a given therapy in oncology, psychiatry, neurodegeneration, and for other disorders.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".