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AI Bridges Multiple Sclerosis Patients to Relevant Clinical Trials

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Clinical trials are a significant milestone in the development of innovative drugs and therapies for people diagnosed with different medical conditions including multiple sclerosis (MS). Many clinical trials are taking place around the world to find and improve treatments and symptom management for MS patients, and many new drugs have been introduced into MS clinical trials over the last decade, giving patients more options for participation. But unfortunately, there is still much to be improved.

The world of MS has a much lower level of awareness of clinical trials, despite the variety of options available. Technology can narrow this gap, making research more accessible to people who are coping with MS. Let’s start with the basics.

What is a Clinical Trial?

A clinical trial is a type of research study that compares the effects of new health interventions on people. These are most commonly new drugs or treatments. However, they may also examine the effects of other health interventions such as diagnostic methods, surgeries or procedures and technological devices.

There are several types of MS-specific clinical trials – those that aim at finding drugs to improve the progressive neurological damage caused by MS, and those that focus on the numerous other symptoms, such as fatigue. Some clinical trials are designed to alleviate symptoms, while others are to shorten the rate and occurrence of relapses and the intensity of them.

Clinical trials are investigating many options and strategies to help patients cope with MS, but how can patients figure out which clinical trial is most relevant to them? How can they access and navigate all the information? There are numerous challenges to overcome in the clinical trial matching world, and the first step is to acknowledge what they are.


Finding a relevant clinical trial can be really tough for MS patients. In some cases, patients are unaware of their options, or don’t have the time to discuss them with their doctor in the short timeslot allotted for each appointment. To add to this, existing clinical trial databases are difficult to use, for several reasons:

1. Exclusionary language – Most databases are designed with doctors in mind. Patients will not feel comfortable if the first thing they see when searching for a trial are medical terms such as extended disability status scale and neurological lesions. Additionally, research terms such as a double-blind study can also confuse layperson patients.

2. Imprecise search terms and results- Clinical trials have developed over time to become far more detailed. The search terms most likely to be used by patients in their search will often lead to a high amount of irrelevant results that will make it harder to find the right trial.

3. Hidden details – A patient may find the perfect trial on a database, but when trying to move forward, they discover additional unpublished requirements. As stated above, study requirements are becoming more complex and naturally, every detail will not make it into the trial description as appears on the database.

These challenges create a significant barrier for anyone, but especially for patients and caregivers who are looking for a relevant clinical trial.

Another challenging factor is that clinical trial databases are updated constantly and rapidly (as they should be), but this lowers the chances of finding the right clinical trial in a short span of time, adding more stress to the equation. Identifying suitable matches quickly is crucial, as the window of opportunity during which patients can meet all the relevant criteria is small.

Lastly, although several databases exist, the information is not consolidated and can be very confusing and overwhelming. Many MS patients simply don’t have quick access to the correct information to enable them to conduct an informed decision in choosing a clinical trial.

Over the past few years, a new approach has emerged to help streamline this process - combining clinical trial databases and patient medical histories with artificial intelligence (AI) and machine learning in order to automate suggested matches for both patients and doctors. These automatic searches are customized not only to search parameters, but to the entire history of specific patients and are helping to address these challenges.

What is AI?

Artificial intelligence (AI) is branch of computer science focused on building smart machines capable of performing tasks that usually require human intelligence. AI aims to simplify the lives of patients, doctors and hospital administrators by doing things that are typically done by humans, but in less time and at a fraction of the cost. AI is not one technology, but rather a collection of them, and is quickly becoming adapted for various healthcare areas.

The AI technologies with the highest value to healthcare include machine learning and natural language processing (NLP). Natural language refers to the overwhelming, difficult-to-analyze technical text that is found in medical databases, and NLP technologies assist us in making sense of the text that the regular patient would find impossible to decipher.

AI for Clinical Trial Matching

Physicians traditionally perform the task of matching patients to clinical trials manually, but AI can efficiently process enormous amounts of patient data with similar or even greater success rates.

AI technology digests de-identified patient information by analyzing various data sources such as documents and electronic health records. The algorithms then capture data points from that information, creating an anonymous profile for each patient, making sense of the patient journey. The system collects hundreds of different types of data points, and then uses it to find relevant clinical trials for individual patients.

Advanced AI technology uses machine learning and clinical trial-specific NLP algorithms to analyze all available trials around the globe in real-time from databases such as the National Institutes of Health (NIH) and the Clinical Trials Transformation Initiative (CTTI). The technology then processes the patient’s medical information and verifies the relevance of clinical trials in the database for that specific patient, finally producing a list of trials specific to a patient’s condition that they can take to their doctor or medical team to discuss.

Clinical trial specialists also supervise the algorithms, constantly keeping them updated. But performing only 5% of the work, while the technology takes care of the other 95%, is a massive time-saver for clinical trial professionals.

In Conclusion                   

The importance of finding new ways to connect eligible patients with relevant trials cannot be overstated, both for the MS community and medical advances. Using modern technology, we can effectively link trial data and patient data to create instant connections that support groundbreaking treatments to help improve the quality of life for patients who need it most.

AI has played an important role in overcoming the challenges of clinical trial matching and will continue to play a growing part in MS treatment. Patients finally have access to all relevant clinical trials, no longer needing to decipher the medical lingo and eligibility requirements all alone because the technology simplifies it. AI is a gift that can help MS patients around the globe find applicable clinical trials in a fast, supportive and effective way.

About the Authors

Irad Deutsch is big data entrepreneur and patent inventor with a strong technical background. He co-founded Belong.Life in 2015 following the loss of his mother to lung cancer. Applying his skills and expertise in big data analytics, machine learning and NLP, Irad and Belong are transforming the way patients and physicians manage the cancer journey, to improve quality of life and treatment outcomes.

Keren Barsky, MA, is Head of Clinical Trials Field at Belong.Life, a developer of social networks for managing and navigating treatments. Keren holds degrees in psychobiology and medical psychology, and over a decade of experience in clinical research in biotechnology. Keren manages Belong’s clinical trials matching service which, to date, has matched more than 8,000 patients, including those with cancer and multiple sclerosis, to relevant clinical trials.