Abstract

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities.

There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease. Today, algorithms are already outperforming radiologists at spotting malignant tumours, and guiding researchers in how to construct cohorts for costly clinical trials.

As a DeSci data infrastructure, BitDoctor AI collects decentralised health data through AI-driven health screenings accessible via smartphone, utilizing blockchain and advanced imaging technology. In just under a minute from your mobile phone's front camera, BitDoctor AI gives a comprehensive health analysis (potentially up to 30 health parameters) and can anticipate problems like heart attacks, diabetes, liver failure and many more other potential diseases, at the same time prescribing potential solutions with just a smart phone. Examples of parameters available:

BitDoctor is revolutionizing healthcare access by cultivating hyper-personalised AI Doctor agent, designed to serve communities lacking medical support and individuals striving for longevity. Leveraging blockchain technology and unique DePIN facility, it enables users to contribute their health data securely to cultivate the AI Doctor agent. This collective intelligence not only enhances hyper-personalized medical support but also paves the way for universal healthcare access globally creating a future where quality healthcare is accessible and affordable.

BitDoctor AI transforms health signals into a crucial insights for the medical industry while keeping your identity anonymous. It starts as a Medical AI Research & Development corporation. The advance machine learning (AI) algorithms is to create non-linear computational models for predicting non-invasive blood biomarkers. The technology is to determine the predictive power of the overall feature set.

Continuous data contribution is important to train the model to better identify the feature with the strongest relations with each biomarkers objectively is to saves lives and save millions if not billions of dollars out of pocket medical expenses for its users. The savings that is brought to protocol, so everyone gets to keep their health in check while securing governance token (DAO) to determine the funding direction on medical longevity researches.