Nobody can deny that the healthcare sector is being completely reshaped by digital technologies. Robotics, 3D printing, nanotechnology, and Big Data – among other incredible inventions – bring the well-being-related domain to a whole new level of sophistication and efficiency.
They allow the medical industry to reduce costs and human errors, but mostly improve treatment processes, data tracking, recollection and analysis, and outcomes quality.
Among all these technologies, Artificial Intelligence has its special place. Coined by computer scientist John Mc Carthy in the mid-1950s, AI combines science and engineering to emulate human thinking patterns.
With Machine Learning and other methods like deep learning, AI is drastically enhancing clinical systems, patient information records, and medical treatments. Also, it’s bringing more efficiency to a major field in healthcare: clinical trials and diagnostics.
An introduction to clinical trials (and its disruption by AI)
Clinical trials (CTs) are research studies or protocols developed to test the safety and efficacy of a drug, medical device, or treatment to diagnose, prevent, or treat a disease. And it’s a pretty expensive business.
Launching a new drug, for example, involves billions of dollars in testing – the value of the clinical trials industry is expected to reach a peak of US $38.68 billion by 2025, with an annual growth rate of 50.2% – and more than a decade of experimental time. With the Covid-19 outbreak, the urge to cut on time and money to diagnose a disease has been made evident. And here’s when AI comes to the rescue.
How is AI used in clinical trials?
The impressive impact of Artificial Intelligence in clinical trials comes from the fact that the success of any CT is based on several steps, most of which are likely to be mimicked or enhanced by computerized reasoning:
– Trial design.
– Process design.
– Patient cohort
– Investigator selection.
– Requirement checks (protocol compliance).
With its capabilities to collect and process large amounts of data, along with its precise ability to predict outputs and generate valuable insights, AI solutions are more than appealing to the clinical trials sector.
Benefits of Using AI and Machine Learning in Clinical Trials
Let’s double-click on some of the main reasons why Artificial Intelligence in clinical trials is changing the game in the healthcare sector.
More accurate diagnosis
The amount of data produced by the healthcare system is huge (we are talking about zettabytes here!) and growing more and more at a pace of 36% per year, mostly due to advances in medical knowledge and information technology. For pathologists and physicians, being able to consolidate and give sense to such an overwhelming landscape of data is a challenge. Thanks to AI, now all of that is possible. Healthcare professionals have AI tools for reviewing patients’ cases, extracting clinically-relevant insights, and diagnosing at a speed and accuracy never seen before.
AI is great for breaking data silos and bringing data sets together from different variables and sources. Not only from multiple data points of the same organization but also from third parties: thousands of trials are taking place on a global scale, many with matching protocols and goals that can be cross-shared to speed up learning and better outcomes. AI is capable of all this, but it’s also useful to cross data from different aspects of one clinical trial in particular, making it possible to learn from early stages like pre-trial planning to final review before publication.
Image recognition enhancement
Images (X-rays, ultrasounds, resonances) are an essential part of medical data and clinical trials. Artificial Intelligence technologies are allowing this field to grow by accelerating images’ reading time and response facing critical cases. With AI and Machine Learning in clinical trials, it is now possible for professionals to browse through a vast variety of images and find patterns they couldn’t identify before. Diagnosis in a lot of medical specialties is being benefited by AI systems on imaging, such as oncology, cardiology, neurology, and more, even in places with a lack of experts or complex medical facilities.
Increase in staff satisfaction (and performance)
Throughout this article, we’ve seen how AI in clinical trials improves not only the quality of research and data analysis but also the efficiency of workflow and operations. Its automation capabilities result in the reduction of repetitive, time-consuming tasks in the laboratory as well as in back office functions with AI-powered administrative software. This situation increases staff satisfaction enormously. Pathologists stop losing precious time on manual work and can re-distribute the effort to more fulfilling activities like cracking complex cases or learning lighter-level skills.
Another upside of AI in clinical trials can be deduced from all the listed above. Undoubtedly, an increase in accuracy, consistency, precision, and process efficiency will result in cost savings. It has been reported that AI applications that streamline workflow. Another significant financial cost for the healthcare industry is – besides inefficient diagnosis and treatment – misdiagnosis. Claims in cases of malpractice have an average cost of almost 400k dollars. Imagine what AI can do regarding cost reduction by eliminating errors in diagnosis with its precision.
Patient’s well-being improvement
And last but not least, AI can save and improve patients’ lives. Personalized care in an overpopulated sector like the healthcare industry is a challenge that can be overcome with the aid of AI and Machine Learning in clinical trials. These technologies can assist in achieving democratization of care, more affordable therapies, reduction of time lost with diagnosis and interventions, and enhancement of treatment, along with faster and better clinical results.
After such amazing advances in a few decades and the speed at which digital technology is evolving, it’s hard to imagine how far AI can go when it comes to helping clinical trials get faster and more sophisticated. Only time will tell. Definitely, there will be a lot of great things ahead to be awe about.