Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review
Long Qian1, Xin Lu1, Parvez Haris2
1Faculty of Computing Engineering Media, De Montfort University, Leicester, UK.
Drug Discovery Today
|March 17, 2025
Summary
Artificial intelligence (AI) models can predict clinical trial outcomes, reducing drug development failures. This review explores AI methods like text embedding and multimodal learning for better trial success prediction.
Area of Science:
- Biomedical Informatics
- Artificial Intelligence in Medicine
- Drug Development
Background:
- Clinical trials are essential but costly and prone to failure.
- Predicting trial outcomes can significantly improve drug development efficiency.
Purpose of the Study:
- To review AI methodologies for forecasting clinical trial outcomes.
- To identify challenges and opportunities in applying AI to trial success prediction.
Main Methods:
- Focus on clinical text embedding techniques.
- Discuss trial multimodal learning approaches.
- Examine AI-based prediction models for trial success.
Main Results:
- AI offers potential to mitigate clinical trial failures.
- Text embedding and multimodal learning are key AI applications.
- Prediction techniques can enhance trial outcome forecasting.
Conclusions:
- AI methodologies can optimize clinical trial success.
- Further research into AI applications is warranted.
- Addressing practical challenges will facilitate AI adoption in drug development.
