For inquiries regarding integration with our models and access to research capabilities through our API, we invite you to contact us. Our API can be hosted on a HIPAA-compliant infrastructure, ensuring the utmost security and privacy of sensitive healthcare data.
Endless Medical API harnesses groundbreaking and patent-pending technologies (USPTO # 20200118691, PCT/US2019/055747) to curate an extensive knowledge database that encompasses synthesized, detailed patient-level data. This database is meticulously crafted by amalgamating robust research, extensive clinical expertise, and thorough analysis of relevant literature. The flexibility of our system allows for rapid updates and regeneration of the database and training data, incorporating new data, diseases, and tests within minutes. In March 2020, we proudly launched our AI diagnostic system for COVID, which, to our knowledge, was the first of its kind at that time. Conversely, large language models (LLMs) and other so-called big-data solutions typically require significantly more time to acquire and train their models using extensive data sources.
EndlessMedical API uses synthetic, individual patient-level data
EndlessMedical API distinguishes itself by eschewing the utilization of "big data" due to its inherent inaccuracies, errors, typos, biases, and associated concerns regarding security, privacy, and data ownership.
Instead, our platform employs synthetic patient-level data as a superior alternative for AI/ML modeling, effectively mitigating the aforementioned issues commonly associated with big data.
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 cutting-edge technologies utilized by Endless Medical API enable the creation of synthetic databases that accurately reflect the actual prevalence of diseases and clinical findings within specific environments.
For instance, the incidence of strep throat may differ between general practice offices and nursing homes. Traditionally, AI and ML models trained on data from one environment cannot be seamlessly applied to another. However, with EndlessMedical technologies, data and models can be effectively transposed from one environment to another, ensuring the adaptability and applicability of the AI and ML models across diverse healthcare settings. This groundbreaking capability opens up new possibilities for leveraging data and models in various environments, enhancing the overall effectiveness and versatility of medical research and practice.
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