Featured Insights & Trends

How Artificial Intelligence Can Enhance Clinical Research

Guest post by health writer and content marketing expert Abhishek Shankhwar.

Clinical trials are the foundation upon which the world of pharmaceuticals and healthcare, in general, depends to get the ideal results. A rough estimate states that it takes around 10-12 years to create a new drug, which requires more than a billion dollars of investment. Even then, there is just a 10% success rate. 

Some of the biggest reasons for this to happen are the withdrawal of volunteers, improper selection of the volunteers, below-par monitoring process, and insufficient funds, amongst others. As a result, there have been several things that have been tried by different sets of professionals and experts, and AI (Artificial Intelligence) has come out to be one of the top solutions.

Let us look at how exactly AI helps improve clinical research:

Clinical trial design

With AI having unparalleled energy to collect data spanning multiple years and analyze it with ease, the design of a clinical trial has taken a new turn. An AI system goes through a ton of scientific and research data, such as patient support programs, past clinical trial records, current records, and post-market surveillance to understand the behavior of the drug and so on.

By studying a combination of successful and failed trials, the AI identifies a pattern which you can use to create a fresh clinical trial design. 

Helps in streamlining selection and recruitment process

One of the biggest issues with clinical trials around the world is the time it takes to select the ideal volunteers. For example, if there is clinical research on developing a drug for breast cancer to be conducted, it takes roughly 4-5 years to just identify a few hundred ideal volunteers from a potential bank of say 40,000.

This is where AI can help you identify the ideal volunteers pretty quickly. Through mining and analysis, it interprets data in such a way that you get the ideal results over much lesser time.  

Helps in reducing population heterogeneity

As a direct result of the point mentioned above, AI helps in reducing population heterogeneity. This is done by predicting whether a patient will last the whole clinical trial/research or not. By studying the data, AI, to a certain extent, can select the volunteers who will last the whole research, thus saving millions of dollars.

Improves processes like Electronic Phenotyping

One of the important processes to reduce population heterogeneity is that of Electronic Phenotyping. However, it is an incredibly complex process and hence does not provide the ideal results. With the integration of AI, the study of data has been faster and more accurate, thus giving positive outputs in a quick time. To do so, it uses Natural Language Processing (NLP) and Machine Learning (ML).

For this, it needs to study a ton of information, but thanks to its robust nature, AI manages to do so pretty quickly and then use the identified patterns to speed things up.

Patient monitoring, medication adherence, and retention

Clinical research is a long and tedious process that requires constant monitoring of how the patient responds and whether they adhere to the medical constraints put on them, and so on. By making the whole system digitized, it not only gets transparent but is also accessible to other systems as well. 

Furthermore, the AI algorithms, when combined with the data collected by the wearable worn by the patient, help you monitor the effectiveness with real-time data. It also helps in determining if there is any risk of volunteers dropping out.

Final thoughts

All the points mentioned earlier directly translate to reduced drug-development time with effective analysis and monitoring of the volunteers throughout the period. The collection and study of all the data points by AI makes clinical research much more successful.

With advanced clinical research processes, the global drug development services market is to witness robust growth in its revenue. However, there is still some time before we can rely totally on AI technology as it cannot be used for all the real-world trials. The integration has to be slow, and that too only after several successful simulations.


Abhishek Shankhwar is a health writer and content, marketing expert. He writes on health and wellness blog Healthystic that aims to uplift people to live a well-balanced life. You can find him reading books, writing blogs, and cooking delicious food in his meantime. Connect with him on LinkedIn.

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