Why Should AI And Machine Learning Be More Prevalent For Clinical Trials

AI and Machine learning are slowly but surely reshaping various industries as we know it, and the potential for the future is immense. The clinical trial industry is not far behind. Clinical trials have evolved exponentially over the years, and more and more electronic and automated components have slowly become a part of the process to ensure that the trials are more efficient than ever and no discrepancies end up affecting the results either. 

When Can We Expect AI And Machine Learning To Become More Common?

While Ai has been in practice since the late 20th century, we still have a long way to go before the technology evolves to a scale where complex human judgment can be automated and utilized for actions such as clinical trials. 

Electronic data gathering and implementation have helped to automate the process to a great extent, making it more effective than ever. Clinical trials now use sensors, electronic diaries, and apps on mobile phones, which can help the institution collect real-time data and curate accurate and precise results. Even so, AI at present is only automating tasks that require little human judgment or brainpower. We are far from where we started, but the journey is still quite long and treacherous. 

Benefits Of Applying AI and ML To Clinical Trials

Of course, as AI and ML become more commonplace in clinical trials, there are many benefits one can reap with their help. Some of the most common benefits of AI and ML to clinical trials include the following:

Get Access To Other Data Banks

Thousands of trials are conducted worldwide every year, and all of these trials have useful insight to offer. When doing your own research, you often have to sift through data banks and reference trials that were conducted previously or those with different data sets. 

Machine learning will make it much easier for you to tap into other data banks and retrieve information that will be beneficial to your trials as well. A fully involved ML system will be able to identify, on its own, other trials and papers that will be useful for your research and bring you the necessary data on its own. 

Effective Testing Of Trials

If you manually test out a clinical trial or begin one without testing all the factors and their feasibility, you might incur heavy losses that will be irretrievable. Therefore, you need an effective method of testing a clinical trial for feasibility before executing it. 

AI will help you to test the clinical trial before you begin execution. AI systems can also help to review clinical trials alongside yours to determine feasibility. The more trials that are conducted, the more comprehensive the system continues to become as well.

Identifying Cohorts

Selecting the right cohort for your study is also of prime importance for all researchers. Many institutions are now making use of electronic records available through previous trials to determine the perfect cohort. With the databases becoming more comprehensive and an AI system tying it all together, it will be much easier for an AI system to identify all the possible cohorts. 

Another way systems can sift through personnel is with the help of information available on social media. AI systems can sift through the data available through them through social media platforms and curate the ideal cohort for your trial. 

Speedy Recruitment

Patients for trials are generally recruited through hospitals and clinics when they fit the selection criteria. AI systems will assist the manual process by analyzing the database and determining the patients that will be ideal for the trial. You can then contact the patient through your preferred methods to recruit them. 

Conclusion

The potential for evolved AI and ML in clinical trials is immense. There is still a very long time before AI and ML can become more widespread and the technology evolves to encompass greater, more complex functions. Even so, AI and ML are still making waves and electronic data collection has allowed for the development of comprehensive databases that can be utilized for clinical trials. The future for both AI and ML is bright, and we can surely hope for the best.