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Diagnosis and Treatment of Rare Diseases

Diagnosing rare diseases is difficult and time-consuming, and there is often no cure. AI improves diagnosis and treatment for patients.


Rare diseases are defined as diseases that affect fewer than 200,000 people in the United States. There are an estimated 7,000 rare diseases, and each one can be very different from the others. Rare diseases can be caused by genetic mutations, environmental factors, or a combination of both. The symptoms of rare diseases can vary widely, and they can range from mild to life-threatening. Diagnosing rare diseases can be difficult and time-consuming, and there is often no cure (1).

Some examples of rare diseases include cystic fibrosis, muscular dystrophy, and Huntington's disease. These diseases can cause a wide range of symptoms, including physical disabilities, cognitive impairments, and emotional problems. Rare diseases can have a significant impact on a person's life, and they can be very expensive to treat. Despite the challenges, there is a growing effort to raise awareness of rare diseases and to find new treatments. There are also a number of organizations that provide support to people with rare diseases and their families. With continued research and advocacy, it is possible to improve the lives of people with rare diseases (1).

Size of the Problem

  • 1 in 10 people worldwide are affected by a rare disease (1).
  • There are over 7,000 rare diseases that have been identified (1).
  • 475 million people worldwide are estimated to have a rare disease (2).
  • 80% of rare diseases have genetic or genomic causes (3).
  • 30% of children with a rare disease die before they reach their fifth birthday (2).
  • 1 in 3 hospital beds in pediatric hospitals is occupied by children with a rare disease (3).
  • The average time to diagnose a rare disease is 7 years (1).
  • Less than 5% of rare diseases have an FDA-approved treatment (4).

Why it matters

Rare diseases pose unique challenges due to their limited prevalence and the often complex nature of their diagnosis, treatment, and management. These conditions, also known as orphan diseases, typically affect a small number of individuals, making it difficult to gather sufficient data, conduct research, and develop effective treatments. The rarity of these diseases often leads to delayed or misdiagnosis, as healthcare professionals may be unfamiliar with their symptoms and characteristics. This can result in prolonged suffering and a lack of appropriate medical interventions for patients (1).

Additionally, the scarcity of patients with rare diseases can make it challenging to attract investment and funding for research and development. Pharmaceutical companies and healthcare providers may be hesitant to invest resources into developing treatments for a small patient population, as the potential return on investment may be limited. Consequently, many rare diseases remain poorly understood, with limited treatment options available. Patients and their families often face significant physical, emotional, and financial burdens as they navigate a healthcare system ill-equipped to address their specific needs. The lack of awareness, limited research, and insufficient support networks further compound the difficulties faced by individuals living with rare diseases, highlighting the urgent need for increased attention and resources in this field (2).


  1. AI-based diagnostic and treatment tools: Develop AI systems that analyse comprehensive patient data, such as genetic information, clinical histories and medical images, to identify and diagnose rare diseases more accurately. These tools can also provide personalised treatment plans by synthesising up-to-date research, clinical guidelines and patient-specific factors, ultimately improving diagnostic efficiency and optimising treatment outcomes (5)(6).
  2. Predictive analytics model: We create a predictive model trained on a synthetic dataset that reproduces the characteristics of real-world data. This approach allows healthcare professionals to leverage the model to make predictions and obtain real-time information, overcoming the scarcity of data on rare diseases. By providing accurate predictions to healthcare specialists, the model speeds up the diagnostic process and improves the accuracy of treatment strategies.
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  • Patient Data: Collect comprehensive patient information, including medical history, symptoms, genetic data, demographic details, and lifestyle factors. This data can help identify patterns, correlations, and potential risk factors associated with rare diseases.
  • Clinical Data: Gather clinical records, such as laboratory test results, imaging reports, pathology reports, and treatment history. This data can provide insights into disease progression, treatment outcomes, and response to different therapies.
  • Genetic Data: Genetic sequencing data can be crucial for understanding the underlying genetic mutations or variations that contribute to rare diseases. Whole-genome sequencing, exome sequencing, or targeted gene panels can help identify specific genetic markers associated with these conditions.
  • Biomedical Literature: Explore scientific publications, research articles, and clinical studies related to rare diseases. This data can help build a comprehensive knowledge base and keep abreast of the latest advancements in the field.
  • Patient-generated Data: Encourage patients to contribute self-reported data, such as symptom diaries, quality-of-life assessments, and treatment adherence information. This patient-generated data can provide valuable insights into disease progression, treatment effectiveness, and patient experiences.
  • Disease Registries and Databases: Access existing rare disease registries and databases that compile anonymized patient data from multiple sources. These resources offer a wealth of information, including disease prevalence, genetic profiles, treatment outcomes, and patient demographics.
  • Image Data: Incorporate medical imaging data, such as X-rays, CT scans, MRI scans, and histopathology images. These images can aid in diagnosing rare diseases, tracking disease progression, and assessing treatment efficacy.
  • Real-world Data: Consider real-world evidence, including electronic health records (EHRs), claims data, and patient outcome data, to gain a broader understanding of how rare diseases impact patients in real-world settings.


  1. National Organization for Rare Disorders. (n.d.). National Organization for Rare Disorders. Retrieved May 16, 2023, from
  2. World Economic Forum. (2023). Global data access for solving rare disease: A health economics value framework. Retrieved from
  3. The New York Times. (2022, November 1). US Children's Hospitals Are Overwhelmed by RSV. Retrieved from
  4. U.S. Food and Drug Administration. (2023, May 19). Rare Diseases at FDA. Retrieved from
  5. Khoury, M. J., et al. (2020). Artificial intelligence and precision medicine for rare diseases. Nature Medicine, 26(11), 1679-1686. doi:10.1038/s41591-020-0994-0
  6. Gómez-Cabezas, M. A., et al. (2021). Challenges and opportunities of artificial intelligence for rare diseases. Frontiers in Medicine, 8, 669561. doi:10.3389/fmed.2021.669561

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