Selected work

Representative Papers

Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies

May 22, 2026

This study addresses the challenge of safely and effectively reducing sample sizes in registered clinical trials while adhering to regulatory requirements. Building upon the FDA’s seven-step risk assessment framework, the work presents the first systematic application of AI model trustworthiness evaluation guidelines to the context of sample size reduction. By constructing prognostic covariates, conducting risk-informed model development and validation, and integrating these with statistical re-estimation methods, the approach recalculates the required trial sample size. Demonstrated in a randomized controlled trial for Alzheimer’s disease, the methodology enabled prospective sample size reduction, substantially shortening trial duration, lowering costs, and accelerating the availability of effective therapies. This provides a generalizable, AI-driven framework for enhancing the efficiency of drug development.

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FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence

May 15, 2026

This work addresses the challenge of effectively integrating population-level aggregate evidence—such as clinical trial statistics—with individual patient data to enhance the reliability of predictive models in clinical decision-making. The authors propose FRESH, a method that operates within an information-geometric framework to apply minimal KL-divergence perturbations to generative models trained on individual-level data, ensuring their outputs exactly match specified aggregate statistics of a target population. FRESH achieves the first unbiased, data-efficient post-training calibration with respect to external summary evidence while preserving the original distributional structure. The approach supports contextual comparisons between single-arm trials and standard-of-care benchmarks, facilitates clinical trial simulation, and enables comparative effectiveness analyses, substantially improving model applicability and generalization in real-world clinical settings.

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Digital Twins as Synthetic Controls in Single-Arm Trials

May 12, 2026

This study addresses the challenge of reliably estimating treatment effects in single-arm clinical trials due to the absence of a concurrent control group. The authors propose a machine learning–based digital twin approach that constructs a synthetic control arm by generating personalized predictions of disease progression for untreated patients. Integrating doubly robust estimation with the U.S. FDA’s artificial intelligence/ML guidance principles, the method leverages historical data for model development and facilitates sample size calculations. Reanalysis of real-world trial data in amyotrophic lateral sclerosis and Huntington’s disease demonstrates that the proposed framework substantially improves the accuracy and robustness of treatment effect estimation, offering a flexible and scalable paradigm for causal inference in single-arm trials.

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Recent publications

Latest Papers

Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies

May 22, 2026

This study addresses the challenge of safely and effectively reducing sample sizes in registered clinical trials while adhering to regulatory requirements. Building upon the FDA’s seven-step risk assessment framework, the work presents the first systematic application of AI model trustworthiness evaluation guidelines to the context of sample size reduction. By constructing prognostic covariates, conducting risk-informed model development and validation, and integrating these with statistical re-estimation methods, the approach recalculates the required trial sample size. Demonstrated in a randomized controlled trial for Alzheimer’s disease, the methodology enabled prospective sample size reduction, substantially shortening trial duration, lowering costs, and accelerating the availability of effective therapies. This provides a generalizable, AI-driven framework for enhancing the efficiency of drug development.

0 citationsRead paper

FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence

May 15, 2026

This work addresses the challenge of effectively integrating population-level aggregate evidence—such as clinical trial statistics—with individual patient data to enhance the reliability of predictive models in clinical decision-making. The authors propose FRESH, a method that operates within an information-geometric framework to apply minimal KL-divergence perturbations to generative models trained on individual-level data, ensuring their outputs exactly match specified aggregate statistics of a target population. FRESH achieves the first unbiased, data-efficient post-training calibration with respect to external summary evidence while preserving the original distributional structure. The approach supports contextual comparisons between single-arm trials and standard-of-care benchmarks, facilitates clinical trial simulation, and enables comparative effectiveness analyses, substantially improving model applicability and generalization in real-world clinical settings.

0 citationsRead paper

Digital Twins as Synthetic Controls in Single-Arm Trials

May 12, 2026

This study addresses the challenge of reliably estimating treatment effects in single-arm clinical trials due to the absence of a concurrent control group. The authors propose a machine learning–based digital twin approach that constructs a synthetic control arm by generating personalized predictions of disease progression for untreated patients. Integrating doubly robust estimation with the U.S. FDA’s artificial intelligence/ML guidance principles, the method leverages historical data for model development and facilitates sample size calculations. Reanalysis of real-world trial data in amyotrophic lateral sclerosis and Huntington’s disease demonstrates that the proposed framework substantially improves the accuracy and robustness of treatment effect estimation, offering a flexible and scalable paradigm for causal inference in single-arm trials.

0 citationsRead paper