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The many benefits of AI in clinical research

Artificial intelligence is one of the greatest inventions humans have come up with in the technology department. It has revolutionized the way we use information, and has helped many people reach new heights. Thanks to newer reforms and regulations, the threat of AI surpassing human potential is impossible. Besides, the claim that AI can surpass human intelligence and potential was false from the beginning. AI in the medical field is quickly becoming the new superhero of the medical field. Additionally, the benefits of AI in clinical research are undeniable.

AI has been used in microbiology testing, and data gathering. Moreover, AI is currently revolutionizing the world of clinical research, making it not only safer, but also more effective. These super smart platforms can optimize the experience of patients and reduce the nuances that come with the clinical trial process. Some of the benefits of AI in the clinical research field include: minimizing dropout rates, and increasing the chances of success. 

Artificial intelligence and the medical industry

Initially, AI was set out to accomplish the three things : 1) business automation, 2) more data analysis, and 3) accurate and trust-worthy decision-making. However, AI is proving itself useful in the medical field by transferring data into its platform, and using it to leverage options without needing human intervention. Collecting, analyzing, and processing data is time consuming, and usually only renders a small benefit. Additionally, AI is able to work with data quickly and effectively, This way, AI can eliminate the imperfection that comes with difficult human labor, and help people in the medical field use their knowledge and time on other, more important areas. 

Benefits of AI in clinical research

AI has also been involved in pilot studies and data analysis. These methodologies are designed to help humans make decisions and use a semi-automated learning to keep track of the results. AI can also work on its own and suggest ways to make data more accurate over time. For instance, MRI scans have recently been equipped with AI that predicts if there is a disease or not and how to fix it. When the AI makes a mistake, and this does happen, the mistake is seen as valuable data, because it provides insight on how to fix the problem and prevent it from happening in the future.

AI can also help doctors and medical researchers notice and keep track of details that they might have otherwise missed. Keeping track of data is extremely demanding and time consuming for a person. However, AI is able to complete the task quickly and without the risk of human error.

The issue with clinical trials:

One of the problems that the medical industry has always faced is clinical trials and its endeavors. This usually lies in the data collection and analysis which can be majorly flawed without the help of thousands of experienced clinical researchers and operators. Moreover, the issue can also lie in the fact that it takes a lot of time to collect the patient’s data, information such as: physiology, health history, past medical consumption, radiomics, etc..

Additionally, logging in data can be demanding labor for the medical researchers. As a result, the medical researchers often end up feeling fatigued or stressed rendering them incapable of doing tasks like interpretation or correlation. 

This causes a cycle whereby the drug that is created is not perfect because the data is not truly analyzed to the deepest details, and the patients don’t take the medicine as prescribed which makes the data result even more untrusted. In the U.S alone, clinical trials spend up to $51 billion every year on testing new drugs. A lot of this money gets wasted because most of the operations don’t reach a final result. The other issue is also the 125,000 patients in the US alone that die, and even more that get hospitalized, for not following the instructions of the medications. 

How Artificial Intelligence can help the clinical trial industry:

Artificial intelligence is a great way to help the enrollment in clinical trials by matching the patients with the right trial for more revealing results. This is because clinical trials account for a big percentage of the pharmaceutical industry’s research budget in treating HIV, cancer, Alzheimer’s, and other diseases. 

AI can help researchers gather more data to figure out the possible correlations. With AI, medical professionals and researchers can devote their time and resources to treating patients with kindness and empathy, finding new ways to cater to their patients, and dedicating time to listening, and caring for their patients. Alternatively, medical researchers would become boggled in the details, leaving them with little, or no time, to treat and care for their patients. Including an AI platform fits best to do the hard labor work and leave the fine touch and human innovation to work and prosper. 

This can also be beneficial because the right drug will reach the right market a lot faster than it would with the traditional methods already in place. The result is that the time, cost, and effort that it takes from the study can be largely cut and reinvested in other areas, or more research. 

The practical ways that AI can be used in to produce better results:

One of the ways that AI can help is by factoring in the patient’s biology, and full data to create a duplicate that exists on the cloud, or on the hardware AI.  This is a superior way to obtain data because then we have a reduced cost of trials by not having to bring the patient every single time for the trial, and it can decrease the risk of the trials because the patient no longer has to experience the effects of the medications. In future research or clinical trials used to assess or create medications, the duplicate can be reused for another operation that also takes less time, effort and money on the part of the pharmaceuticals and the patients themselves. 

Genomics and proteomics can also be used to solve problems in real time since the data can be reused. The possibilities are endless because the patient can also reenter their reaction to further aid the AI to come up with more accurate and helpful answers. 

One of the visions that AI developers have is by using sensors that can insert into the bloodstream while uploading data into the AI platform. This will act as a sensor to track the patient’s ECG, blood cell count, hydration levels, body temperature, respiration, activity level and even hormone levels in live time along with other variables taken into consideration. This is then processed to create feedback and generate results. 

How the medical research field and the clinical trials can harness the power of AI:

The way that trial clinics can truly benefit from AI is by operating a system that can actually match their patients a lot more accurately. The percentage of clients who have cancer enrolled in a clinic trial program is very small. This can be solved by the help of AI to generate patients depending on the criteria. Mayo clinic has actually been able to do this with the help of IBM Watson. They were able to recruit valuable patients for their clinic trial. The AI pulled a list of various variables that were found to be priority, and in few minutes the AI was able to locate and identify the best possible patients. 

This was done to cut the cost and time it took for the research, and the results was satisfying. Veradigm and Microsoft Azure are also working to do the same thing which is actually not that hard for AI to do. If you consider some of the things that AI is capable of, locating patients to the perfect clinical trials is a small task for the platform.

The system requires a set of information that can be easily gathered. Such information may include: lab results, doctor’s notes, medical records, and other identifying details. In the case of Mayo Clinic, using AI was very beneficial, as the rate and accuracy was unique to AI, and could not haven possible with only the effort of medical researchers.

Although this is only the beginning, AI can actually take the time to develop the knowledge it has to upgrade the decision making system that it has and ultimately make better decisions the more time it operates. Software engineers can also work on giving a feedback system which automatically makes the AI self-automated. We are really looking at a bright future because the power of AI in the medical research field has finally arrived, and it can open the door for new possibilities. AI can reduce the the word load, and let the doctors and medical researchers use their fine touch, experience, and intuition- things the AI does not have, and cannot possibly mimic. Ultimately, the goal is to let the AI leverage the results to find better patients for the clinical trials, allowing the doctors and medical researchers to care for these patients in the best way possible. 

IBM has done a good job of leveraging AI to do various things, such as finding better patients. They were able to reduce the labor of their workers, maximize the accuracy of the clinical trial patients, and analyze the data. 

This carries the potential to make the challenging parts of clinical trials manageable, thus increasing the average monthly enrollment over time. 

A chat-bot can also help your clients diagnose themselves through a series of questions the chat-bot will be programmed to ask. The patient inputs their information to the system where it is analyzed, stored, and processed. This information is added to the doctor’s notes, and other valuable data. This chat-bot alone can actually help the patient and the clinical trial come up with a list of possible patients, that will be most valuable to the research conducted. The answers would go to the desk and reveal the answers displayed. This serves two things, first: it alarms or updates the patient and their doctor on the health situation or the progress which can be used to make better decisions later down the road. The second benefit is that it can act as a data for the clinical trial programs to notify them that there is and ideal patient whom they should consider. 

The best way to leverage AI to bring better results for patient matching in clinical trials: is an example of AI serving clinical researchers, and making all the aforementioned benefits a reality. opts for a unified platform of AI that takes into consideration the exact information that is needed and create a much more efficient campaign testing with the accurate audience targeting. 

The clinician in can identify his or her points of eligibility and criteria before the testing begins. This combined with a good medical record can make it easy for the administrative force to determine who are the potential patients, and how can they reach them quickly and effectively. AI has proved itself an efficient tool, and pharmaceuticals are quickly picking up on the benefits of AI programming.

The indications used by AI can be easily changed, the platform can take future references and more information about the patients, and redirect the next clinical trial phase depending on those variables. This process can take as little as a few seconds for, but for us humans, it would take us days and weeks to come up with this given the fact that the results can be unused and not important. is an innovative solution to the clinical trial recruitment problem. The recruitment process can take more than 10 weeks, and there is no need for this delay with AI. Therefore, AI from proves to be a better solution for any clinical trial program because it will cut down on time and effort, while improving the efficiency of the results. 

Additionally, this can decrease the chances of failure and can help improve the number of FDA approvals for drugs being tested, which is roughly one in ten human subjects. With that said, AI can help in patient recruitment by matching them through the right clients, optimize the clinical trials, and even come up with better solutions. 

AI can be trained to cater to the patient’s medical data and the criteria that the clinical trial has set for it. These can be: symptoms they are looking for, diagnoses, test results, and other variables. The results can be generated in a few minutes with a ranking from the most eligible to the least eligible. This can help optimize the chances and save time in the research. 

Some of the hopes and goals that AI engineers have in terms of clinical trial success rate is to reduce the 15 year span that it takes to make a new drug and put it out there on the market. SAI technicians also hope to reduce the cost of billions of dollars that come mostly from the nuances of making a drug, and not from the physical manufacturing in the labs. In addition to that, the goal is to reduce or eliminate the risk of failures that come from poor recruiting or time spend on the project. has been able to create an AI that is very easy to use with controlled selection sampling, and effective selection. The results are fascinating- they were successful at refining the audience to the needs of the study protocol, causing a spike in high-quality leads. AI can work anytime, it lets the medical staff work on things that demand human ingenuity to. With the AI, the chances are endless. 

Researchers and companies have already started using AI:

Sponsors did not waste any time when they found out that AI can be used to reduce the large cost of clinical trials as well as the time it takes. As a result, AI has indeed helped identify the patients that are perfect for the criteria given by reducing population heterogeneity. As a result, the companies have been able to double if not triple the chances of finding the right patient for the program in a shorter time. This, in return, has helped drug makers create more drugs in less the time with less budget. 

The clinical trial administrations and organizers have teamed up with software engineers and AI experts to predict the rate of dropouts and save up time. 

The future of the medical industry:

Thanks to AI, the possibilities have been stretched. Machine learning can help in accelerating the rate at which the patients get the medical treatment, and this can be extremely vital for many lives, since a lot of patients depend on medication for their life. It can also show the power of the medical organization that is in charge of the clinical trials since they will be able to generate more patients and make more effective drugs to the public in a much shorter time. As a result, this can attract a lot of doctors that are interested in working with the companies. These doctors will also be utilized more effectively, since they will not have to worry about the small details. Patients will also be more satisfied with the outcome result since they will receive the right medication for their case, and more quality attention from their doctors and medical team. 

This can revolutionize the pharmaceutical industry and give a serious edge in the competitiveness that the pharma industry is known for. AI, if harnessed correctly, can definitely improve the quality of work of whoever is using it.

With AI and big data getting better every time, effectively matching patients to clinical trials is an easy task. It would be quite inconsiderate if AI were not used to cut the cost, time, and labor it takes for the medical staff to generate patients for the clinical trials. AI can help in a lot of other areas that can shake the ground of the medical industry, companies like BAYER have been working on creating a twin copy that remains on the cloud, and this copy is created through the help of sensors and patches that will upload data to the twin on the system. What this means is that one can use the twin instead of the physical patient to run experiments while leveraging those experiments in real to cut the cost and the risk of any medical operation. Health and IT can be a serious combination for the future of creating a better world for people with diseases such as HIV, cancer, Alzheimer’s, and more. It has never been a better time to implement AI into the medical research. AI will not only help optimize the clinical trial experience in all of its facets, but it can also help the pharma industry and doctors come up with long unanswered questions and defeat the challenges and nuances that often rise up in the medical research.  

The medical industry has a lot more to offer than anyone can think of, so it’s best to let the AI do the heavy labor work more effectively and focus on using the skills and knowledge that make doctors the heroes they are. 


BOOKBINDER, M. (2017, September 29). The Intelligent Trial: AI Comes To Clinical Trials. Retrieved April 11, 2018, from

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