Big-data sets often fail to adequately represent rare diseases, resulting in an underrepresentation of such conditions. Conversely, they tend to overemphasize billable diagnoses and notable positive findings in physical examinations and medical histories, leading to a disregard for normal and negative findings. Furthermore, obtaining big-data for research or commercial purposes requires costly and time-consuming Institutional Review Board (IRB) approvals. While synthetic patient-level data is not without flaws, it can be a viable alternative to big-data in certain situations. Endless Medical API effectively utilizes synthetic patient-level data for AI/ML modeling, showcasing the practicality of this approach. It's worth noting that large language models (LLMs) pose their own challenges. They are expensive to train or update and necessitate massive training corpuses. Additionally, it takes time for corpuses and knowledge to be published and made available for training LLMs.

The technologies utilized by Endless Medical API enable the creation of synthetic databases that accurately reflect the prevalence of diseases and clinical findings within specific environments. For instance, the occurrence of strep throat in a general practice office differs from its occurrence in nursing homes. Traditionally, AI and ML models trained on data from one environment are not easily applicable to another. However, with EndlessMedical technologies, data can be effectively transposed from one environment to another, allowing for greater adaptability and flexibility.