What is the clinical trial process, and how can artificial intelligence improve it?

Before a new medication or treatment arrives on the market, it goes through a lengthy and extensive testing phase known as the clinical trial process. These steps determine whether or not a drug is safe to use and effective in tackling the problem it’s addressing – and they also provide new information concerning any potential side effects that might occur.

It’s not cheap to conduct these trials, with the average cost standing at approximately $2 billion per drug. This is because clinical trials involve huge numbers of volunteers – sometimes hundreds of thousands – and take place over the course of many years. Obviously, this involves a Herculean effort on the part of the biotech and pharmaceutical companies, and every precaution needs to be taken to ensure the trial isn’t too risky for the volunteers.

Despite such a huge investment in terms of money, time and effort, only around one in ten clinical trials is actually successful. This means that the one drug that receives go-to-market approval needs to recoup the costs of the nine failed trials – meaning that it will need to earn around $15-20 billion in order to allow the pharmaceutical company to break even. As a result, the actual cost of medications goes up, affecting real-world patients.

So how do we address this problem in a way that’s sustainable, ethical and reliable? One option gaining increasing traction across the field is the use of data analytics to increase clinical trial success – but how does this work?

By using artificial intelligence, it is possible to predict whether or not a trial is likely to be successful based on a number of variables – and the pharmaceutical company can decide whether or not this particular treatment should be pursued, or if they should explore alternatives before engaging in the clinical trial process. As a result, fewer trials are conducted, with only the ones that are likely to be successful actually carried out – saving huge amounts of time and effort.

Using outcome-predicting technology which accurately determines which drugs might be successful, pharmaceutical companies can save money in the long run – reducing the necessary recoupment for the successful treatment and ultimately lowering the cost for the patient. It’s in nobody’s best interests to have unsustainably expensive medications on the market: patients will simply look elsewhere for other solutions, and pharmaceutical companies will end up in the red, with their treatment not helping anyone.

Lowering the cost of clinical trials whilst simultaneously increasing their success rates has enormous benefits for the patient. There’s a reason the healthcare industry is gradually moving towards a more extensive use of artificial intelligence in the drug development process: their intention is to improve the patient’s quality of life, and cheaper, more successful clinical trials take us one step closer to this goal.

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