# Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling

arXiv: https://arxiv.org/abs/2502.09688 · HTML: https://mlsanity.com/paper/2502.09688/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://mlsanity.com/paper/1909.12475/) (2019)
- [MAISI: Medical AI for Synthetic Imaging](https://mlsanity.com/paper/2409.11169/) (2024)
- [A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?](https://mlsanity.com/paper/2112.00639/) (2021)
- [Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges](https://mlsanity.com/paper/2407.00116/) (2024)
- [Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting](https://mlsanity.com/paper/2403.06835/) (2024)
- [The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts](https://mlsanity.com/paper/2301.08719/) (2023)
- [RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning](https://mlsanity.com/paper/2308.14899/) (2023)
- [Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models](https://mlsanity.com/paper/2304.03218/) (2023)

## Built on by (most cited first, 4 of 4)

- [NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging](https://mlsanity.com/paper/2512.18038/) (2025)
- [Foundation Models for Medical Imaging: Status, Challenges, and Directions](https://mlsanity.com/paper/2602.15913/) (2026)
- [Non-intrusive Body Composition Assessment from Full-body mmWave Scans](https://mlsanity.com/paper/2605.08306/) (2026)
- [2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models](https://mlsanity.com/paper/2606.21414/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) is poised to transform healthcare by enabling personalized and efficient care through data-driven insights. Although radiology is at the forefront of AI adoption, in practice, the potential of AI models is often overshadowed by severe failures to generalize: AI models can have performance degradation of up to 20% when transitioning from controlled test environments to clinical use by radiologists. This mismatch raises concerns that radiologists will be misled by incorrect AI predictions in practice and/or grow to distrust AI, rendering these promising technologies practically ineffectual. Exhaustive clinical trials of AI models on abundant and diverse data is thus critical to anticipate AI model degradation when encountering varied data samples. Achieving these goals, however, is challenging due to the high costs of collecting diverse data samples and corresponding annotations. To overcome these limitations, we introduce a novel conditional generative AI model designed for virtual clinical trials (VCTs) of radiology AI, capable of realistically synthesizing full-body CT images of patients with specified attributes. By learning the joint distribution of images and anatomical structures, our model enables precise replication of real-world patient populations with unprecedented detail at this scale. We demonstrate meaningful evaluation of radiology AI models through VCTs powered by our synthetic CT study populations, revealing model degradation and facilitating algorithmic auditing for bias-inducing data attributes. Our generative AI approach to VCTs is a promising avenue towards a scalable solution to assess model robustness, mitigate biases, and safeguard patient care by enabling simpler testing and evaluation of AI models in any desired range of diverse patient populations.