FaceHealth – AI Doctor for Health Screening

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

Our proposal advances the BGI mission by democratizing access to health screening via a decentralized platform. By integrating AI-driven facial analysis with secure data aggregation, we empower communities with real-time health insights while preserving privacy. This proactive, community-sourced approach promotes early detection of health issues, fosters public health resilience and contributes to building a transparent, inclusive AI ecosystem.

Our Team

I am a dedicated computer science student with a focus on AI, computer vision, and mobile app development. As an independent developer building FaceHealth, I draw inspiration from my connection with bitdoctor.ai and its innovative approach to decentralized health screening. I actively seek mentorship and collaboration with industry experts to continuously improve my skills and ensure that my project upholds the highest standards of privacy, security, and ethical practice.

The core problem we are aiming to solve

Current health screening methods are centralized, expensive, and often inaccessible, especially during public health crises. Traditional systems suffer from delays, limited reach, and privacy risks, leading to missed opportunities for early intervention. In underserved regions, the absence of immediate, reliable screening results in preventable health complications. Our project addresses these issues by decentralizing the screening process and building a constant learning AI model that adapts to new health threats, providing a rapid, low-cost, and privacy-preserving solution.

Our specific solution to this problem

I propose a mobile application that transforms personal health screening into a community-powered network. By using advanced computer vision algorithms, the app analyzes facial features to detect potential health indicators—such as signs of fever, fatigue, or other visual biomarkers that may correlate with common illnesses. The process is simple: users open the app, scan their face, and receive near-instant feedback along with recommendations for further action if needed.

All data is anonymized and securely transmitted via a decentralized network, ensuring user privacy while enabling aggregated public health insights. Our system employs blockchain technology to verify data integrity and incentivize participation through token rewards. This encourages widespread adoption, turning every user into a node within a larger public health monitoring system. Continuous learning is built into our AI, so the model refines its accuracy over time through federated learning without compromising personal data. Ultimately, our solution provides an efficient, scalable, and secure mechanism for early detection of health issues and fosters a proactive community response in times of health emergencies.

Project details

Overview:
FaceHealth DePIN is designed to revolutionize the way health screening is conducted by shifting from a centralized model to a decentralized, community-driven network. In today’s global landscape, timely and accessible health screening is more critical than ever. Our platform enables users to perform rapid, reliable health checks simply by scanning their faces using a mobile device.

Technology and Process:
At its core, the solution leverages cutting-edge computer vision and machine learning technologies. The mobile app captures a short video or a still image of the user’s face. This image is processed in real time by an AI model that has been trained on diverse datasets to recognize subtle visual cues indicative of common health issues. The app then returns an immediate assessment, such as potential fever detection or fatigue levels, along with guidelines for follow-up actions. All the while, user privacy is maintained through end-to-end encryption and decentralized data handling.

Data from individual screenings is anonymized and aggregated on a blockchain-based platform. This decentralized ledger not only secures the data against breaches but also ensures transparency and trust, as each data transaction is verified by the network. Moreover, by distributing the processing load, our system minimizes the risk associated with centralized data repositories.

Community Empowerment and Incentivization:
The decentralized nature of FaceHealth DePIN means that every user contributes to a broader public health dataset. This aggregated data can be used by local health authorities and community organizations to monitor health trends, predict outbreaks, and deploy resources more effectively. To further incentivize participation, our platform incorporates a token-based rewards system. Users earn tokens for regular screenings and for contributing data, which can be exchanged for benefits within the ecosystem or redeemed at participating healthcare providers.

Ethical Considerations and Data Privacy:
Recognizing the sensitive nature of health data, our solution is built with a strong commitment to privacy and ethical standards. We integrate zero-knowledge proofs and trusted execution environments (TEEs) to ensure that personal data is never exposed during processing. Our federated learning approach allows the AI model to continuously improve without compromising individual privacy.

Scalability and Future Applications:
FaceHealth DePIN is designed to scale seamlessly from local communities to national health systems. As adoption increases, the platform will generate a wealth of anonymized data that can be harnessed to develop advanced public health analytics, refine predictive models, and ultimately improve health outcomes on a larger scale. Future updates may include integration with wearable devices and other biometric sensors to expand the range of health indicators monitored.

Conclusion:
In summary, our project is a transformative approach to public health screening that leverages decentralized technology, AI, and community participation. By making health screening accessible, efficient, and privacy-centric, FaceHealth DePIN will empower individuals and communities to take charge of their health and contribute to a safer, healthier society.

Needed resources

I am looking for additional expertise in regulatory compliance and advanced AI model training (especially in reinforcement learning).

Existing resources

I have developed a working prototype of the mobile app and established initial partnerships with local health organizations. Additionally, I have also bitdoctor.ai investor as my mentor and am currently seeking funding from Solana Foundation Malaysia to further scale and enhance the project.

Open Source Licensing

We plan to adopt the MIT License to promote open collaboration and transparency while ensuring that contributions and modifications are easily integrated into the community-driven project.

Links and references

• bitdoctor.ai – Inspiration for decentralized health screening.
• "Facial-Recognition-Based Applications in Disease Diagnosis" – Comprehensive review.
• "Evaluation of a Camera-Based Monitoring Solution Against Regulated Medical Devices" – Measuring vital signs.
• "Using artificial intelligence to improve public health" - Narrative Review

Was there any event, initiative or publication that motivated you to register/submit this proposal?

Describe the particulars.

Yes – Recent global health challenges and the lessons learned from the COVID-19 pandemic highlighted the urgent need for accessible, real-time health screening.