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The benefits of A.I. in clinical research.


Benefits of AI in clinical research and the medical field

Artificial intelligence is one of the greatest inventions humans have come up with in the technology department. Due to new reforms and regulations, the threat of AI surpassing human potential is not possible. Besides, the claim that AI could surpass human intelligence was false from the beginning. Additionally, AI is quickly becoming the new superhero for the clinical research field. This article will discuss the benefits of introducing AI into the clinical research field.  

Artificial intelligence and clinical research

Three tasks were initially set out for AI:

  1. Business automation
  2. Data analysis
  3. Accurate and trust-worthy decision-making.

However, AI is additionally proving it can be useful in the medical and clinical research field. AI is able to transfer data into its super-smart platform, and use it to leverage options without requiring human intervention. Collecting, analyzing and processing data is time consuming, and usually only renders a small benefit. However, AI is able to work with data quickly and effectively. This way, AI can eliminate the imperfection that inevitably accompanies tedious human labor, while also helping people in the medical field use their knowledge and time on other, more important areas. 

Benefits of AI in clinical research

AI is currently revolutionizing the world of clinical research by making it safe and more effective. For example, AI has been used in microbiology testing and data gathering. These two examples only provide small insight into the many benefits of introducing AI into clinical research. 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:

  • minimized dropout rates
  • lower risk of error
  • higher patient satisfaction
  • increased chances of success

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. Additionally, AI can 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, and how to fix it.

The main goal of AI is to help doctors and medical researchers. Often times this means helping medical staff 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.


While it is rare for an AI to make a mistake, it does happen. However, the mistake is seen as valuable data because it provides insight on how to fix any existing problems and prevent them from happening in the future. However, AI in clinical research is usually risk-free. Speeding up the enrollment process by using AI, rather than presenting set backs, offers safer and more accurate clinical trial results.

A doctor is in the center of the image, surrounding him are lines connecting him to patients, upcoming appointments, and new leads.
AI connects doctors with the moving world around them

The issue with clinical trials

Clinical trials have always presented an issue in the medical field. Data collection and analysis can be majorly flawed without the help of thousands of experienced clinical researchers and operators. Moreover, it takes a lot of time to collect the patient’s data and information. Additionally, logging in data can be demanding labor for medical researchers.

Medical researchers can often end up feeling fatigued or stressed leaving them unable to do vital tasks like interpretation or correlation; as a result of this, data is not fully analyzed. Therefore, the drug produced is not perfect. Additionally, patients often do not take the medicine as prescribed which makes the data collected even more untrustworthy. Moreover, patients often cancel appointments or fail to report all their symptoms.

In the U.S alone, clinical trials spend up to $51 billion every year on testing new drugs. A lot of money gets wasted because most of the operations don’t reach a final result.

Artificial Intelligence as a solution

The benefits of AI in clinical research are plentiful. Artificial intelligence can speed up the enrollment process by matching the right patients to the right clinical trials in order to produce more revealing results. Clinical trial enrollment accounts 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 possible correlations. With AI, medical professionals and researchers can devote time and resources to treating patients with kindness and empathy by finding new ways to cater to their patients, and dedicating time to listening and caring for their patients. Alternatively, medical researchers become boggled in the details, leaving them with little time to care for their patients. AI do the hard work, platforms serve no other purpose. AI is crafted to help medical researchers, and by virtue of doing so, also help patients receive high quality attention and care.

The benefits of AI in clinical research also mean the right drug will reach the right market a lot faster than with traditional methods in place. The result is that the time, cost and effort that it takes for the study can be largely cut and reinvested in other areas, or more research. 

Wearable health devices and artificial intelligence

A wrist is shown with a wearable health device on.

Wearable health devices help track a patients vital signs and other indicators, so as to ensure their health. People are already accustomed to wearing similar devices for fitness, and personal health empowerment. It is common to see people wearing watches that track their heartbeat, how many steps they’ve taken, and their weight gain or loss.

WHDs and clinical research

WHDs have the ability to collect vital signs such as: blood pressure, heart rate, body temperature, respiration rate, electrocardiograms, blood oxygen saturation, blood glucose, motion evaluation, etc. While patients are engaged in a variety of daily-activities, information is collected. Therefore, medical staff are able to better understand how these activities affects their patients.

Additionally, these devices can also help clinical researchers obtain more accurate data. For example, patients often take their medication at the wrong time, resulting in untrustworthy research results. With WHDs doctors can send alerts to their patients when it is time for them to take their medicine.

To add, WHDs limit the amount of times a patient has to visit a medical center. Information such as vital signs and other symptoms can be collected with WHDs. Potential patients may exclude themselves from the participant pool because they feel overwhelmed with the schedule of the clinical trial. However, many eligible patients will reconsider joining the study if the amount of visits can be reduced.

The information collected can be very valuable AI platforms are able to automatically track data collected from WHDs. After the AI platform has sorted the data, it automatically send it to medical centers for evaluation.

The power of AI

Trial clinics can use AI to operate a system that can match their patients a lot more accurately. Very few cancer patients are enrolled in clinical trial studies. However, there is a growing desire to produce better cancer treatments. This can be solved with the help of AI platforms, which target eligible patients and provide them a variety of options for clinical trials based on their diagnosis and location.

Mayo clinic, with the help of IBM Watson, was able to recruit valuable patients for their clinic trial. The AI pulled a list of priority variables, and in few minutes was able to locate and identify the best possible patients. The rate and accuracy was unique to AI, and could not haven possible with only the effort of medical researchers.  Veradigm and Microsoft Azure are working on similar projects.

The future of clinical research with AI

Although, this is only the beginning; AI can develop the knowledge it has to self-upgrade its own decision making system, and make better decisions the more time it operates. Software engineers can create a feedback system which automatically makes the AI self-automated.

We are 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 work load, and allow the doctors and medical researchers to focus on 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 Watson, chat-boxes, and other new technologies

IBM Watson 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. IBM Watson offers developers the ability to build their own chatbox. It also offers businesses the option to simply implement the one that suits their needs.

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. 

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. uses a unified platform that considers the exact information needed, and creates a more efficient campaign.

The clinician using can identify his or her points of eligibility and criteria before the campaign begins. This, combined with good medical records, can make it easy for the administrative force to determine who are the best potential patients, and how can they reach them quickly and effectively.

The indications used by AI can be easily changed. Further, the platform can take references and additional information about patients, and redirect the next clinical trial phase depending on those variables. For, this process takes as little as a few seconds; however, medical staff can take days or weeks to complete this task. is an innovative solution to the clinical trial recruitment problem. The recruitment process can take more than 10 weeks, but, with AI, there is no need for this delay. Thus, AI from is best solution for the clinical trial recruitment problem. This solution will cut down on time and effort, while improving the quality of the results. 

Long term benefits of AI

The use of AI can decrease the chances of failure, and can also improve the number of FDA approvals for drugs being tested. With that said, AI can help in patient recruitment by matching them through the right clients, optimize the clinical trials, and come up with better solutions. 

AI softwares can be trained to cater to the criteria that clinical trial staff have set for it.

These can be:

  • symptoms they are looking for
  • diagnoses
  • test results
  • other variables

The results can be generated in minutes, and can also rank patients from most eligible to least eligible. This can help optimize the chances of success

AI in the future

AI engineers hope to reduce the long time it takes to make a new drug and put it on the market. The average is 12 years. SAI technicians also hope to reduce the cost of making a drug. The average is currently 2.7 billion. In addition to that, the goal is to reduce or eliminate the risk of failures that come from poor recruiting. 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. 

Doctor is seen working with an iPad

Sponsors acted quickly when they discovered the benefits of AI in clinical research. As a result, AI has helped identify the patients that are perfect for the criteria given by reducing population heterogeneity. Companies have been able to increase the chances of finding the right patient for the trial in a shorter time. Additionally, 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 clinical research

Thanks to AI, the possibilities have been stretched. Machine learning can help in accelerating the rate at which patients get medical treatment, and this is vital because a lot of patients depend on medication for their life. AI can also help the clinical research field become more organized. This will lead to more effective drugs reaching the marker in a shorter time.

Additionally, the promising future of clinical research will attract a lot of high quality doctors. These doctors can provide top notch care for patients because they will not have to worry about extensive and tedious paperwork. In return, patients will be more satisfied with the outcome because they will receive quality treatment, along with the right medication to treat their case.

AI can revolutionize the pharmaceutical industry. If used correctly, AI can improve the quality of work of companies, while also improving patients’ quality of life.

The benefits of AI in clinical research can make effectively matching patients to clinical trials an easy task. The issues discussed with persist and continue to pester if AI is not used to cut the cost, time and labor it takes for medical staff to generate patients.

It has never been a better time to implement AI into medical research field. 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.


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

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