We truly have come a long way from our past as a civilization. Using data from historic SARS and MERS studies, cooperation within the healthcare and virology community, willing volunteers and modern technology, we have been able to create vaccines for a disease that hasn’t even turned two yet. Clinical trial phases that would have taken decades in the past have been completed successfully within a matter of months. So, how did we do it? Undeniably, technology has played a big part in pushing the process into hyperspeed. AI, in particular, has been put to good use to fight the deadly coronavirus. AI’s capabilities of real-time data analysis and information retention seem tailor-made for dealing with situations like these. So, how does AI make the process of clinical trials faster? And why does it seem like AI-driven clinical trials—despite AI’s obvious shortcomings in the field of healthcare—are here to stay? Read on to find out.
Understanding Medical Records and Pathology Reports
Knowing which individuals are fit for the purpose of being volunteers, or test subjects, for a clinical trial can be a challenging task. The medical records of such individuals need to be properly assessed to know whether it is safe for them to be the metaphorical guinea pigs in clinical trials or not. A doctor’s handwritten medical note or automated hospital records may have the relevant information about a patient’s current health condition, medication usage and other details in an unstructured and free-flowing way.
This is where Natural Language Processing (NLP), one of AI’s capabilities, can be useful. NLP empowers connected healthcare systems to understand and deeply analyze spoken and written text, no matter how bad the handwriting or how scattered or unstructured the information. With its incredible pattern recognition abilities, AI and NLP can understand what is written in a doctor’s note, even in cases where different professionals use different terms to describe the same thing. Additionally, an individual’s medical history can be viewed by AI-powered healthcare systems to compile detailed reports about their participation eligibility in trials.
AI’s evolution will allow the technology to systematically carry out the inclusion or exclusion of subjects based on their health and medical records. Accordingly, future AI systems will be able to generate useful data from unannotated records too. To achieve that, hospitals will need to maintain online patient records in standardized, coded formats so that the time and confusion associated with understanding information on the internet is reduced.
In short, AI can accelerate a clinical trial by speeding up the process of candidate eligibility determination through their medical records.
Monitoring Subject Compliance Remotely
One of the hardest things for vaccine testers and manufacturers is keeping track of a test subject when they are at home. As you know, the creation of new vaccinations and drugs may require test subjects to follow certain rules regarding their diet and medication over a specified period. That is why, it is believed that clinical trial participants turn into metaphorical black boxes when they leave the clinic as the healthcare scientists’ understanding of their health and dietary records are non-existent after that point. Test subjects may or may not be obligated to stay within the research facility during the entire duration of a clinical trial. As a result, pharma companies and healthcare professionals risk stalling the research and development phase of a drug or vaccination if this problem is not dealt with.
The knock-on effects of this issue will be, as you can imagine, disastrous for everybody involved. The experts wouldn’t have any information about the participants following protocols and medications. As a result, the real efficacy of a drug or vaccine will not be known. This would give rise to a mismatch between data generated from clinical trials and real-world usage information.
Here, AI can be used for the remote monitoring of test subjects. With due consent, drug manufacturers can deploy computer vision-enabled cameras to facilitate real-time subject monitoring when they are in their homes. So, the drug makers and associated healthcare professionals can keep a close eye on their subjects’ diet, physical activity, medication adherence and health condition after consuming an under-trial drug. Most importantly, the monitoring teams must ensure that a subject’s privacy is not violated.
During the 24X7 monitoring, the healthcare experts can instruct their subjects to make certain changes to specific aspects of their lifestyle for understanding the effects of their drugs on their bodies. Structured Q and A sessions can be conducted to know how the participants are feeling at certain points during the day.
Thereby, computer vision and remote monitoring can be useful to make AI-driven clinical trials less complicated and much quicker.
Creating Failsafe Clinical Trial Processes
Once AI-driven clinical trials become the mainstay, healthcare professionals and data scientists can collaborate to create the actual blueprint or design of the trials. Generally, clinical trials follow a set list of protocols that are designed in advance based on past data or other information. Any issues or roadblocks that emerge during a clinical trial can cost a lot of time (probably months) and money (millions of dollars), especially if such problems end up sending the trials back to square one. Therefore, it is necessary that the design of a clinical trial is created with the right amount of foresight and usage of historical data.
While designing the protocols, researchers use data from various sources, such as compiled data, compliance information and details from other clinical trials. AI-powered systems can make the process absolutely failsafe due to their razor-sharp speed and accuracy in data collation and analysis.
Adopting a big data-driven method for AI-driven clinical trials can result in better design for trial protocols. AI’s primary components, NLP and computer vision, can scan the entire internet for information included in research papers and journals compiled by health experts and scientists. Additionally, data present in rival companies’ websites and government websites can be used for the design purpose too. As we know, going through billions of data pages on the internet and analyzing all of them carefully is nearly impossible for even a large group of researchers. Using AI to design the clinical trial procedure can allow organizations to save money as well as time. At the same time, AI also reduces the time taken for the design itself.
Personalizing Drug Formulation for Patients
One of the main aims of using AI-driven clinical trials is the introduction of precision and personalization in the field of drug making. Generally, researchers who conduct clinical trials may lack a greater understanding of requirements on an individual level. As a result, pharma companies may not understand if an adverse reaction in one of their many test subjects was caused due to their drug or any underlying health condition in them. The introduction of AI in this process can increase our knowledge about each subject. Since each test subject is expensive, researchers and drug makers may want to get a lot of conclusive information from each of them.
Understanding which phenotypes show what kinds of reactions to a drug or vaccination can allow researchers and manufacturers to create personalized, tailor-made products for patients belonging to each phenotype.
Personalized medicines can be created by obtaining an in-depth understanding of each subject’s reaction to a certain medicine. As we know, there can be several differences between the body types of two individuals. The involvement of AI in the process can allow organizations to move from a generalized, population-based approach to drug creation to making medicines and vaccines for each body type. The remote monitoring tools used earlier can be put to use again for this purpose. Data about a patient’s vital signs and other aspects, such as blood sugar can help personalize the medicine-making process.
For now, the target of making personalized medicines is still a distant dream. However, with AI, we can not only accelerate the clinical trial process, but increase its quality quotient.
As we can see from the above advantages of AI-driven clinical trials, the introduction of the technology in the process can seriously increase the pace of the procedure while also giving it a quality boost. AI may have its fair share of shortcomings in the field of healthcare for now, but one cannot overlook the sheer benefits and applications it has in the same field.