Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.
Younghoon SeoHyemin JangHyejoo LeePublished in: Life (Basel, Switzerland) (2022)
Clinical trials for Alzheimer's disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.
Keyphrases
- artificial intelligence
- clinical trial
- machine learning
- big data
- deep learning
- phase ii
- current status
- open label
- cognitive decline
- study protocol
- double blind
- public health
- phase iii
- magnetic resonance imaging
- small molecule
- high throughput
- electronic health record
- randomized controlled trial
- computed tomography
- climate change
- single cell
- risk assessment
- data analysis
- amino acid