AI’s impact on clinical data

For a long time, the only way scientists within the pharmaceutical industry could interpret clinical data was through the use of human intelligence, lots of manpower and significant amounts of time.

With the growing popularity of artificial intelligence, however, things are gradually beginning to change, and we are seeing a sharp shift in the way clinical data is being processed and understood.

It has never been easier to organize data and pull only the relevant analytics from clinical data to help scientists and the pharma industry as a whole do their jobs more efficiently and effectively.

Here are just a few ways that artificial intelligence is already making a significant difference to clinical data in 2021.

Optimize Clinical Trials By Analyzing Past Trial Data

One of the biggest challenges facing clinical trials at the moment is finding the right patients at the right time to take part in drug trials.

When relying on human intelligence alone to decipher data to decide upon the perfect patients, this can be an extremely time consuming process. The hours spent finding the necessary information can also be extremely expensive.

Not only is it about finding the perfect patients, however; those in charge of clinical trials also need to make sure that their clients are located close to the trial site itself because this increases the success of said trials, adding yet more time onto the preparation process of clinical trials.

To overcome this barrier to successful clinical trials, however, there are companies who’re working on solutions to include artificial intelligence as part of this process.

One such company consists of a collaboration between Novartis and Quantum Black, who have come together to share their expertise and created a product called Nerve Life.

This product analyses data about the performance of physical trial sites from trials gone by to predict future trial performance.

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This allows clinical trial organizers to better optimize their trial up front by choosing a suitable location with suitable participants, avoiding waste for patients and making the whole trial more effective overall.

Helps To Find Reliable Clinical Trial Patients Faster

As aforementioned, relying on human intelligence to decipher large quantities of clinical data can be an extremely time consuming process.

In fact, it’s predicted that enrolling the ideal candidate within these trials takes an average time of 7.5 years.

This can have a significant impact on the pharma industry, as it severely limits their ability to create new treatments and cures for some of the most difficult to treat conditions.

In fact, Digital Authority Partners reports that around 80% of clinical data trials fail to meet enrolment deadlines due to the amount of time it takes to find the right candidates.

These catastrophic statistics could soon change as AI grows in popularity within the pharmaceutical industry, however, which would improve patient care in the long run.

This happens as artificial intelligence can streamline and automate the process of clinical trial matching by using a specific set of criteria and matching patients with recommended trials within a far shorter period of time.

This could provide us with far more clinical data than we currently have access to within a far shorter period of time, making it a far more powerful asset within the pharma industry than it currently is.

Helps To Make Sense Of Clinical Data

Fundamentally, the biggest impact on the pharma industry in the age of artificial intelligence is that the technology can be used to make sense of clinical data.

The truth is, at this moment in time the pharma industry simply has access to too much big data that it cannot easily decipher.

By imploring the use of automated algorithms and cleverly-programmed machine learning technology, however, we could soon see this changing.

In fact, artificial intelligence can—and is—being used to help breakdown big data into analytics that can easily be understood using human intelligence by scientists within the pharma field.

Artificial intelligence can also help to make sense of clinical data by removing whatever isn’t relevant, allowing people to focus solely on the data that furthers their trials instead.


The use of artificial intelligence within the pharma industry is extremely important, and with the technology continuing to grow in popularity, we will only see it furthering its integration into the field over the next few years.

The bottom line is that using artificial intelligence, clinical data can become more powerful, having an impact on far more situations than it currently is able to.

Going forwards, we will see more innovative treatments for previously unexplored, rare conditions, and better patient care for people across the board, as our data continues to be cleverly managed with this technology.