Why Using Artificial Intelligence in Clinical Trials Becomes the New Normal
In 1994, Dr. Kevin Hughes and his colleagues wanted to test a treatment for early-stage breast cancer in older women. Even though around 40,000 women in the US could qualify for this trial every year, it took Hughes and his team a whole five years to recruit 636 participants.
Some time later, Mayo Clinic was planning another study involving breast cancer. The researchers relied on IBM’s Watson for artificial intelligence (AI)-powered clinical trial patient matching and reported an 80% increase in monthly enrollment. If Dr. Hughes would’ve had access to such technology, he would’ve recruited enough participants sooner.
Nowadays, pharmaceutical companies benefit from healthcare AI development services to facilitate their clinical studies’ planning and execution. The global AI-based clinical trials solution provider market is on the rise. It was valued at $1.3 billion in 2021 and is forecast to grow at a CAGR of 22% from 2022 to 2030.
So, what else can AI do to benefit clinical trials? And what challenges could your organization expect on the way to the technology’s implementation?
Studies show that clinical trials of new drugs last nine years on average and cost around $1.3 billion to carry out. The cost of failed clinical trials, meanwhile, ranges between $800 million and $1.4 billion. And the fact that 90% of all drugs end up failing clinical trials only complicates the matter.
In traditional clinical trials, doctors and researchers manually look for participants, and patients have to be physically present to enroll and undergo evaluation. The treatment also occurs on site through scheduled visits. This remains a safe approach to developing new remedies. However, it is slow and lacks the flexibility required to compose complex therapies and address the needs of smaller population segments that are often heterogeneous.
Additionally, this approach doesn’t have the capacity to integrate and process data from hospitals, research centers, private practices, and patients’ homes. Researchers would struggle with participant recruitment, and would request patients to visit trial sites for systematic condition reviews and monitoring, which could increase the chances of patient dropout.
Artificial intelligence and its subtypes can help resolve these issues.
AI can integrate data from multiple sources, including electronic health records (EHRs), research papers, past clinical trials information, and special medical case studies. It can also handle the continuous stream of data from personal medical devices.
AI-driven clinical trial technology can aggregate, clean, process, manage, and visualize all this information in a way that helps clinicians understand a given disease and the potential that different chemical compounds offer in countering it. While predictive analytics in healthcare helps foresee how patients can react to the proposed remedies.
Gaining access to insights derived from all this information in a timely manner will empower researchers to make more informed decisions fast. Here is how AI can benefit different aspects of clinical trials.
Artificial intelligence has many benefits in the healthcare sector.
