arXiv Machine Learning
Jun 25
OncoSynth: Synthetic data generation for treatment effect estimation in oncology
arXiv:2606. 25762v1 Announce Type: new Abstract: In oncology, access to patient-level data is often restricted.
Artificial intelligence surrogates for treatment effect estimation with before-and-after data
The paper proposes a method for estimating treatment effects using AI-generated predictions as surrogates, applied to paired before-and-after measurements for each treated individual. By comparing AI predictions before and after treatment, the approach can identify the average treatment effect on the treated under certain technical assumptions, even when clinical outcomes are never observed for treated subjects. When assumptions are questionable, the authors introduce prediction‑powered inference that corrects bias with a small set of observed outcomes, and validate the method with synthetic and cardio‑oncology data.
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arXiv Statistics ML
Aug 21
A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
arXiv:2608. 19501v1 Announce Type: cross Abstract: Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments.
arXiv Machine Learning
Jun 5
Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction
arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.
arXiv AI
Jun 17
Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation
arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.
arXiv AI
Jun 2
A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.
arXiv Machine Learning
Sep 10
Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes
arXiv:2609.06294v1 Announce Type: new Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...
arXiv Machine Learning
Jul 31
Psych-ECA: A Reproducible Semi-Synthetic Benchmark for Synthetic Control Arms in Longitudinal Psychiatry
arXiv:2607. 27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties regulators care about: not only how accurately a method reconstructs untreated trajectories, but whether its uncertainty is calibrated, whether it is robust to the informative observation times common in mental-health records (sicker patients are seen more often), and what false-positive rate it induces in go/no-go trial decisions.
arXiv AI
Sep 15
Causal multi-modal AI for personalized chemosensitivity prediction
A causal multi-modal AI model was developed to predict personalized chemosensitivity in breast cancer patients using routine pathology and clinical data. Trained on 9,141 patients from nine countries and validated on 1,994 patients from three countries, the model produced treatment-specific recurrence probabilities with near-perfect calibration and strong prognostic discrimination over 5- and 10-year horizons. It outperformed existing recurrence-score tests and could reduce chemotherapy prescriptions by 30% while maintaining recurrence-free rates, with predictive performance also transferring to non-breast cancers.
Hugging Face Trending Papers
Sep 16
Rethinking How We Evaluate Methodological Progress in Health AI
Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which conditions. However, such...
arXiv AI
Sep 17
Rethinking How We Evaluate Methodological Progress in Health AI
The study re‑implements 12 AI algorithms for electronic health records within a unified framework and evaluates them on MIMIC‑IV and NWICU datasets. It compares expert‑authored clinically meaningful tasks with randomly generated tasks, finding that pairwise algorithm comparisons transfer well across task families and datasets, yet clinically meaningful tasks show stronger task‑method interactions. The results also reveal that newer algorithms do not consistently outperform older ones, with gradient‑boosted trees remaining highly competitive when combined with modern EHR representations.
arXiv Machine Learning
Sep 22
CLARITY: Medical World Model for Guiding Treatment Decisions by Simulating Context-Aware Disease Trajectories
arXiv:2512.08029v4 Announce Type: replace Abstract: Clinical decision-making in oncology requires forecasting how disease evolves under treatment, yet most AI systems remain static predictors that ca...
Hugging Face Trending Papers
Aug 13
Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology
Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical observations, medication changes, repeat interventions, and physiological measurements are recorded asynchronously and can change risk assessment over time.