# TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model

arXiv: https://arxiv.org/abs/2404.01273 · HTML: https://mlsanity.com/paper/2404.01273/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 87 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
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- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Deep Recurrent Survival Analysis](https://mlsanity.com/paper/1809.02403/) (2018)
- [Making Pre-trained Language Models Great on Tabular Prediction](https://mlsanity.com/paper/2403.01841/) (2024)
- [MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records](https://mlsanity.com/paper/2308.14089/) (2023)
- [Integrating Large Language Model for Improved Causal Discovery](https://mlsanity.com/paper/2306.16902/) (2023)
- [Differentiable Scaffolding Tree for Molecular Optimization](https://mlsanity.com/paper/2109.10469/) (2021)
- [MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization](https://mlsanity.com/paper/2010.02318/) (2020)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [Reinforced Genetic Algorithm for Structure-based Drug Design](https://mlsanity.com/paper/2211.16508/) (2022)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records](https://mlsanity.com/paper/1709.01648/) (2017)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability](https://mlsanity.com/paper/2402.17887/) (2024)
- [Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering](https://mlsanity.com/paper/2403.04890/) (2024)
- [A Continued Pretrained LLM Approach for Automatic Medical Note Generation](https://mlsanity.com/paper/2403.09057/) (2024)
- [EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders](https://mlsanity.com/paper/2012.10020/) (2020)
- [LLM-driven Multimodal Target Volume Contouring in Radiation Oncology](https://mlsanity.com/paper/2311.01908/) (2023)
- [Uncertainty Quantification on Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2401.03482/) (2024)
- [Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision](https://mlsanity.com/paper/2105.06293/) (2021)
- [Obesity Prediction with EHR Data: A deep learning approach with interpretable elements](https://mlsanity.com/paper/1912.02655/) (2019)
- [ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases](https://mlsanity.com/paper/2207.10670/) (2022)

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- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
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- [ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.20358/) (2026)
- [SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction](https://mlsanity.com/paper/2408.05696/) (2024)
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- [Data Augmentation Through Random Style Replacement](https://mlsanity.com/paper/2504.10563/) (2025)
- [TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction](https://mlsanity.com/paper/2404.13235/) (2024)
- [Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding](https://mlsanity.com/paper/2505.19825/) (2025)
- [CATCH: A Modular Cross-domain Adaptive Template with Hook](https://mlsanity.com/paper/2510.26582/) (2025)
- [TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets](https://mlsanity.com/paper/2407.00631/) (2024)
- [A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives](https://mlsanity.com/paper/2506.16594/) (2025)
- [The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records](https://mlsanity.com/paper/2603.14003/) (2026)
- [LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients](https://mlsanity.com/paper/2508.10021/) (2025)
- [Language Interaction Network for Clinical Trial Approval Estimation](https://mlsanity.com/paper/2405.06662/) (2024)
- [DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning](https://mlsanity.com/paper/2410.02023/) (2024)
- [RHealthTwin: Towards Responsible and Multimodal Digital Twins for Personalized Well-being](https://mlsanity.com/paper/2506.08486/) (2025)
- [How to Train Text Summarization Model with Weak Supervisions](https://mlsanity.com/paper/2409.00098/) (2024)
- [TrialSynth: Generation of Synthetic Sequential Clinical Trial Data](https://mlsanity.com/paper/2409.07089/) (2024)
- [GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction](https://mlsanity.com/paper/2504.05454/) (2025)
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## Abstract (arXiv)

Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several years to complete, with a high probability of failure during the process. Recently, there has been a burgeoning interest in virtual clinical trials, which simulate real-world scenarios and hold the potential to significantly enhance patient safety, expedite development, reduce costs, and contribute to the broader scientific knowledge in healthcare. Existing research often focuses on leveraging electronic health records (EHRs) to support clinical trial outcome prediction. Yet, trained with limited clinical trial outcome data, existing approaches frequently struggle to perform accurate predictions. Some research has attempted to generate EHRs to augment model development but has fallen short in personalizing the generation for individual patient profiles. Recently, the emergence of large language models has illuminated new possibilities, as their embedded comprehensive clinical knowledge has proven beneficial in addressing medical issues. In this paper, we propose a large language model-based digital twin creation approach, called TWIN-GPT. TWIN-GPT can establish cross-dataset associations of medical information given limited data, generating unique personalized digital twins for different patients, thereby preserving individual patient characteristics. Comprehensive experiments show that using digital twins created by TWIN-GPT can boost the clinical trial outcome prediction, exceeding various previous prediction approaches.