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Rare Diseases: Increasing Diagnosis and Treatment with AI

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

by Jose Zea•2 min read

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

Problem

It is estimated that between 7,000 and8,000 rare diseases affect the global population, with an incidence of fewerthan 1 in 2,000 people. Latin America accounts for approximately 10% of globalcases [1].

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Problem Size

  • There are over 50 million people in Latin Americalive with a rare disease, yet 95% of these conditions lack approved treatments.
  • Families spend between 30% and 50% of their income on diagnostics and care, leading to debt and financial instability [3].
  • Globally, rare diseases affect 1 in 10 people (475 million), with significant pediatric impact, including 30% of affected children not surviving past their fifth birthday and a third of pediatric hospital beds occupied by these patients.

Solution

Predictive AI Model for Early  Detection:

Analyzes clinical and genomic data to  identify patterns associated with rare diseases, significantly reducing  diagnostic times.

Key functionalities:

Processes medical records to identify red flags.

Simulates clinical scenarios based on medical literature.

Prioritizes critical cases with a  personalized approach.

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Opportunity Cost

Reducing diagnostic time by 60-80% wouldsignificantly lower associated costs, enabling more effective resourceredistribution and timely access to treatments.

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Impact

  • Clinical: Faster and more accurate diagnoses improve patient prognosis and quality of life, reducing severe complications.
  • Economic: Early treatment reduces healthcare costs and alleviates the financial burden on families.
  • Social: Empowers medical communities and patients, promoting greater inclusion and awareness of rare diseases.

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Data Sources

The model design is based on what was reported in the studies by Khoury et al. (5) and Gómez-Cabezas et al. (6), which detail the importance of the chosen variables in the diagnosis and treatment of rare diseases. These studies ensure that the synthetic data mimics the real-world scenarios that doctors face when treating rare diseases, providing a reliable foundation for the model's predictive capabilities.

Citations

  1. CEPCAL. (2024). Rare Diseasesin Latin America and the Caribbean. Retrieved from https://www.cepcal.org
  2. France24. (2024). Living with aRare Disease: A Diagnostic Odyssey and Struggle for Treatment. Retrievedfrom https://www.france24.com
  3. Noticiero Médico. (2024). TheImportance of Addressing Rare Diseases in Latin America. Retrieved from https://www.noticieromedico.com
  4. Rare Diseases Organization.(2024). Report on Latinos and Rare Diseases. Retrieved from https://rarediseases.org
  5. Repetto, G. (2020). GeneticAspects of Rare Diseases in Latin America. Journal of the Argentine Society ofGenetics. Retrieved from https://sag.org.ar
  6. Khoury, M. J., et al. (2020). Artificial intelligence and precision medicine for rare diseases. Nature Medicine, 26(11), 1679-1686. doi:10.1038/s41591-020-0994-06.
  7. 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

Video

https://www.tella.tv/video/clx0d2mg000lv09lb97h4freu/