# Applications of artificial intelligence in drug development using real-world data

arXiv: https://arxiv.org/abs/2101.08904 · HTML: https://mlsanity.com/paper/2101.08904/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 59 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks](https://mlsanity.com/paper/1901.01451/) (2019)

## Built on by (most cited first, 7 of 7)

- [DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery](https://mlsanity.com/paper/2505.13940/) (2025)
- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
- [Momentum Aggregation for Private Non-convex ERM](https://mlsanity.com/paper/2210.06328/) (2022)
- [Clinical Trial Information Extraction with BERT](https://mlsanity.com/paper/2110.10027/) (2021)
- [Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey](https://mlsanity.com/paper/2309.12177/) (2023)
- [A Scalable AI Approach for Clinical Trial Cohort Optimization](https://mlsanity.com/paper/2109.02808/) (2021)
- [An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic](https://mlsanity.com/paper/2111.14623/) (2021)

## Abstract (arXiv)

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.