Is Artificial Intelligence The Best Solution For Diagnosing Rare Diseases?

Diseases affecting a small number of individuals as opposed to the general population are called rare diseases. In nature, rare diseases seldom occur thus making the method of procuring the correct diagnosis immensely difficult for medical specialists and patients.

While there is a small number of rare disease cases, the impact can be quite confounding. There are more than 6,000 known rare diseases and at least one of them affects an estimate of 3.5 percent of the global population at any time. People who have been suffering from rare diseases could benefit from an accurate and timely diagnosis.

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What Are the Challenges in Diagnosing Rare Diseases?

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Approximately, 40 percent of children with rare inborn diseases are misdiagnosed at the onset. There are some cases where patients have been misdiagnosed more than once and wait nearly five years on average to be given an accurate diagnosis, even in countries where health systems are already highly developed. Consequently, a delay in correct treatment and incorrect management of the rare disease occurs.

Accelerating and improving the diagnostic method may potentially help around 446 million people suffering from a rare disease across the globe. Estimatedly, half of this number are children. People involved in technology spaces and rare diseases need to take appropriate measures in enabling a faster diagnostic method.

How Can AI Revolutionize Rare Diseases Diagnosis?

With the remarkable technological advancements introduced to the medical world, technology is well-positioned to embark on the challenges in diagnosing rare diseases. For instance, genomic analysis along with other omic technologies has greatly improved the diagnosis of rare diseases. The data gathered from these technologies show a significant increase in the information retrieved which needs to be analyzed, integrated, and selected. 

Machine Learning (ML), a subtype of AI, supplies algorithms with the capability of learning from data. As per the Food and Drug Administration (FDA), AI is the engineering and science of creating intelligent machines while ML is an AI tool wielded to train and design algorithms for learning and acting on data. ML algorithms are divided into the following categories:

  • Supervised learning - the ML algorithm is provided with input data together with the correlated target data. The task of the ML algorithm here is to detect the relationship between the provided input data and correlated target data. For example, finding out whether a chest X-ray corresponds to a patient with tuberculosis or not.

  • Unsupervised learning - the ML algorithm is given input data but there is no target data. The algorithm’s task here is to reveal if the data inputted has an underlying structure. An example of unsupervised learning is the clustering of similarities among disease parameters or patients.

  • Reinforcement learning - the job of ML algorithms in this category is to detect the appropriate action to raise a reward. For example, are the dynamic treatment regimes.

Over the years, ML algorithms are proven effective in various areas. Using AI algorithms in diagnosing rare diseases is one of the many ways artificial intelligence revolutionized not only the diagnostic method but the whole biomedicine world.

What Areas of Concerns Does AI Address in Diagnosing Rare Diseases?

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Detecting Rare Diseases Early

Using AI in gathering the correct data, medical practitioners may be able to provide an early diagnosis to people who possibly have a rare disease. With the right data, the possibility to determine if a patient’s symptoms are for common illnesses or rare diseases. 

Classifying Patients

Classification techniques such as decision trees, neural networks, and random forests are some of the methods that can be AI-trained. These methods are used to find out the correct cluster to which a patient belongs. Additionally, machine learning methods can also divide patients into various subgroups like having similar characteristics or diagnoses. 

Improve Data Pooling

The transfer learning method is used to pool data from electronic medical records (EMR) and develop a model of patients to determine the needed information for a correct diagnosis. By using this method, it will pool a sufficient amount of data that will help medical practitioners formulate a timely and accurate diagnosis.

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What AI Platforms Are Used in Diagnosing Rare Diseases?

While not all biotech companies are open to using AI technology in diagnosing rare diseases, some embraced the groundbreaking technology. Below are some platforms developed by the companies utilizing artificial intelligence:

Emedgene’s Cognitive Genomics Intelligence

Emedgene, a company based in Israel, developed a platform able to scan DNA data of a person suffering from a rare disease. Additionally, it uses natural language processing (NLP) to interpret medical literature. With the use of Emedgene’s platform, the diagnosis of a patient can be speeded up by finding a correlation between a patient’s condition and their genetic variants. By using this method, geneticists will just review the results instead of analyzing data from scratch.


Face2Gene is a smartphone app using machine learning algorithms and brain-like neural networks in classifying distinct facial features of people’s photos with neurodevelopmental and congenital disorders. The patterns gathered from the photos are used to generate possible diagnoses and give a list of various options.

Medical practitioners use Face2Gene as support even though the app is not designed to generate definitive diagnoses.

Fabric GEM

Fabric GEM is a platform using advanced AI to automate the diagnostic method for rare diseases. The platform examines sequencing data together with the patient's clinical data and other complex structural forms. Using Fabric GEM, the genetic diagnosis process is cut down from days to minutes by allowing clinical teams to focus on the most plausible possibility.

The Fabric GEM platform was developed to address the need for speedy results of genomic interpretation for NICU patients. Additionally, the platform is utilized to scale genomic testing for people suffering from a rare disease.


Dx29 was developed to facilitate rare disease analysis and diagnosis. The platform undergoes four phases: phenotyping, genotyping, phenotype refinement, and final evaluation.

Dx29 processes first the reports from various sources to draw out symptoms then code them. Afterward, the platform starts a learning algorithm automatically to cluster mutations of patients according to their correlation with the phenotypes. Then Dx29 will suggest new symptoms for the medical professional to contrast. Finally, the platform will generate a classified list of probable pathologies with a designated score.

Medical practitioners as well as people suffering from rare diseases deserve an accurate and timely diagnosis. Welcoming artificial intelligence technology for diagnosing rare diseases could greatly contribute to achieving this goal.

What are your thoughts on using AI for diagnosing rare diseases? Sound off your comments below!

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