There are two main causes for the high failure rates of clinical trials: Substandard recruiting and patient selection techniques and the failure to effectively guide patients during trials. These high failure rates are a major reason why the drug development cycle is so inefficient.
Pharma research and development has steadily increased over the years, but extra spending has been unable to kick the trend of fewer new drugs reaching the market. However, the emergence of artificial intelligence (AI) and big data could transform vital steps of clinical trial design.
These technologies have finally matured to the point where they can be trusted to influence decisions in real-life situations.
Big Data’s Impact on Clinical Trials
Pharmaceutical companies can use big data to find the ideal solutions for speedier and more straightforward clinical trials. Big data can also be used to establish more objective evidence while also cutting down on the costs of tests.
The road to greater accuracy and precision will be paved by consistent and unbiased data and quantifiable scales of measurement that are automatically collected. The gathered evidence can objectively assess and demonstrate a treatment’s clinical effectiveness, performance, side effects, and safety.
Big data encourages remote data monitoring, generating a vital foundation for discovering deeper insights on drugs. It’s also possible to incorporate technologies that collect, store, and process patient data.
How AI Automates Clinical Trials
Artificial intelligence can be used to automate a variety of clinical trial processes. This is especially true in the pharmaceutical sector. Let’s examine three applications in particular:
- Text Mining
Text mining, which is AI-driven, is a way to automatically discover new information by extracting it from past clinical trial information. The newly discovered information can then be used to inform the current clinical trial planning. This is a great way to eliminate unnecessary study expenses without hurting the reach and effectiveness of the study.
- Optimization and Design
This AI application also makes use of previous trial design and execution data. The data is used to influence the study design process by delivering insights, significantly increasing the likelihood of an optimized and successful clinical trial design.
Access to real-time data ensures pharmaceutical companies can efficiently and effectively modify trial designs during the study. Additionally, using AI to analyze real-time data is perfect for recognizing issues with poor or ineffective studies. These studies will be stopped sooner than they would otherwise, saving a great deal of money.
Proper patient recruitment is one of the biggest challenges clinical trials face, but AI has the potential to remarkably improve the effectiveness of this area. Traditionally, companies have relied on physicians for identifying eligible patients for trials. However, AI can analyze patients’ electronic health records and other relevant attributes to expertly match patients with upcoming trials.
Physicians must perform this task manually, but AI can efficiently process enormous amounts of patient data with similar or even greater success rates. This application can even encourage patients to make additions to their personal health records to better optimize the list of trials they match. Not only does this give patients more control over their data, but it also makes the experience more personal.