EndlessMedical technologies are capable to generate synthetic, individual patient-level data, and use it for AI/ML modeling.
The innovative technologies (USPTO # 20200118691, PCT/US2019/055747) employed by Endless Medical API enable the creation of a comprehensive knowledge database. This database comprises synthetic, patient-specific data generated from a combination of research, clinical expertise, and literature. It is highly recommended that individuals review the patent information to gain a thorough understanding of the benefits and drawbacks associated with EndlessMedical API technologies.
EndlessMedical API uses synthetic, individual patient-level data 
EndlessMedical API refrains from utilizing "big-data" due to its inherent drawbacks such as inaccuracy, errors, typos, biases, and concerns related to security, privacy, and data ownership. Instead, we employ synthetic patient-level data for AI/ML modeling, allowing us to address some of the challenges associated with big data.
Furthermore, we can ensure the utmost security and compliance* by hosting our products on HIPAA compliant, HITECH, SOC2 and SOC3, NIST, and PCI certified hosting platforms. (*if requested by our research partners).
The knowledge database and models of the EndlessMedical API are exclusively developed by Lukasz Kiljanek MD, who incorporates literature reviews whenever necessary. This knowledge can be readily adapted to different clinical environments, such as an intensive care unit or an emergency department, as per specific requirements.
In the future, the integration of data from multiple experts and diverse sources, including research papers, will be implemented to enhance the robustness of the medical knowledge database. This, in turn, will improve the sensitivity and specificity of the AI/ML models utilized by EndlessMedical API.
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.
