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Diagnosing rare diseases is difficult and time-consuming, and there is often no cure. AI improves diagnosis and treatment for patients.
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].
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.
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.
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].
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.
Reducing diagnostic time by 60-80% wouldsignificantly lower associated costs, enabling more effective resourceredistribution and timely access to treatments.
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.
