Clinical trials are an essential tool in the advancement of medical science. However, they’re also expensive and time-consuming. In this article, we explain how AI has changed clinical trials for the better, saving time and money while improving patient outcomes and compliance.
Designing Clinical Trials
Using machine learning to understand patient data can help researchers design trials that are more likely to succeed in the first place. Machine learning is used to design new clinical trials for vaccines and other treatments, including cancer immunotherapy. Vaccine trials are an excellent example of how these technologies have the potential to change medicine forever.
This is because many factors go into creating successful vaccines. The vaccine’s genetics, its delivery system and even how it will be stored all play a role in determining whether a vaccine works. A tool like machine learning can analyze all of these variables at once, so researchers don’t have to spend time designing different tests for each one individually.
Text Mining of Literature To Find Unique Insights
Another way AI is changing the drug development process is through machine learning and natural language processing to find patterns in text.
For example, rather than humans having to read large amounts of information, you can use machine learning to find meaningful insights that may otherwise go unnoticed.
“From kick-off to product release, our dedicated team of DCT, Clinical Operations, and Therapeutic experts will partner with you to determine the best approach for your unique vaccine needs.” says Medable clinical experts.
Improved Patient Selection
AI can help you find suitable solutions for patients for your clinical trial by looking at the bigger picture.
- Pick patients with similar diseases and offer quality care for positive data analysis.
- Is likely to benefit from the treatment being tested in your trial (based on their response to past treatments).
- If patients maintain consistency, take their medications, follow instructions, and regular checkups can result in adherence and retention during treatment.
Selection of Sites
AI is used to assist with site selection. The site selection process is highly manual, meaning there are many opportunities for human error.
For example, suppose a study manager chooses one site over another simply because it’s closer to their hometown and not based on predefined criteria. In that case, it could lead to a biased sample of participants. AI helps eliminate these biases by using algorithms to analyze all potential sites objectively. So no favoritism is involved in determining which ones will be chosen for the study.
In the past, researchers and physicians had to observe patients in a clinical trial at an established facility. However, AI has made it possible to monitor patient progress remotely. This allows participants to continue treatment without traveling long distances or finding childcare, which is especially helpful for rural or developing communities.
There are many other ways that AI has helped clinical trials. The key takeaway is that AI is here to stay; everyone should embrace it. But, as the technology continues to evolve, so will its applications in medicine. So you can expect more doctors and health care providers to use AI for diagnosis and treatment decisions.