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Poulav Bhowmick
Poulav Bhowmick

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AlchemistAI- A Revolutionary step towards AI in healthcare

Title: Transforming Healthcare with AlchemistAI: Innovations in Privacy-Preserving Collaborative AI

Team Members:

  • Poulav Bhowmick (devfolio- bpoulav2003)
  • Sagar Rana (devfolio- SoarinSkySagar)
  • Binayak Mondal (devfolio- noobmaster69k)
  • Debanjan (devfolio- debanjan_20)


In today's healthcare landscape, access to high-quality medical data and preserving patient privacy are paramount challenges. Our team, comprising Poulav Bhowmick, Sagar Rana, Binayak Mondal, and Debanjan, embarked on a groundbreaking project called AlchemistAI. Our mission was clear: revolutionize healthcare by addressing the scarcity of quality medical data and ensuring data privacy in collaborative AI model training.

Project Overview:

AlchemistAI marries generative AI and federated learning techniques to create a robust framework. This framework not only generates realistic synthetic medical data but also facilitates collaborative AI model training across various healthcare providers while upholding patient privacy and data sovereignty.

Addressing Medical Data Scarcity:

We recognized the limited availability of comprehensive medical data. To combat this, we harnessed generative AI models. These models learn intricate patterns and distributions from real medical data like images and electronic health records (EHRs). By training these models on limited real data, we can generate vast amounts of synthetic data that closely mimics real data without compromising patient privacy.

Ensuring Privacy in Collaborative Training:

Our project incorporates federated learning techniques, which allow AI models to be trained across multiple sources without sharing raw data. This decentralized approach ensures that sensitive medical data remains within local institutions while still contributing to the collective AI model's improvement.

Key Project Steps:

  1. Data Preparation: Real medical data undergoes rigorous preprocessing, including anonymization and compliance checks with privacy regulations.
  2. Generative AI Model Development: We crafted specialized generative AI models tailored to the medical domain, exploring various architectures and training strategies.
  3. Federated Learning Framework Setup: A robust framework was established for collaborative training across diverse data sources.
  4. Model Training and Evaluation: Our generative AI models were trained using federated learning, generating synthetic data used to train AI models for healthcare applications.

Technology Stack:

Tech Stack Elaboration:

Our project's tech stack comprises a robust set of technologies and platforms that synergize to deliver the AlchemistAI framework:

  1. TensorFlow: TensorFlow serves as the backbone for developing and training both global and federated learning models.
  2. Pytorch: Pytorch is instrumental in crafting generative adversarial networks (GANs), a key component for generating synthetic medical data. Its deep learning capabilities, coupled with GAN architecture, allow for the creation of realistic medical data samples.
  3. Python, Pandas, and NumPy: Python acts as the primary programming language due to its versatility and extensive libraries. Pandas and NumPy facilitate data manipulation, preprocessing, and numerical computations essential for handling medical data.
  4. Next.js and Flask: Next.js and Flask are utilized for web development, creating user-facing interfaces and APIs. These interfaces empower healthcare providers to interact with the system seamlessly, contributing to collaborative model training.
  5. ChromaDB and MongoDB: ChromaDB, utilizing Pinecone, alongside MongoDB, serves as the backbone for efficient storage and retrieval of medical data and model parameters. These databases ensure scalability and reliability in managing vast datasets securely.

Challenges and Solutions:

  1. Federated Learning Complexity: Implementing a secure and efficient federated learning system demanded overcoming technical hurdles. Custom aggregation techniques were developed within TensorFlow Federated to manage decentralized training effectively. This involved establishing secure communication channels and robust validation mechanisms to preserve data privacy and integrity.
  2. Data Privacy and Regulatory Compliance: Handling sensitive medical data required strict adherence to privacy regulations. Stringent data anonymization techniques were implemented, and necessary approvals and consents were obtained before processing real medical data. The use of synthetic data generation and federated learning mitigated privacy risks further. Generating GANs using Pytorch: Designing and training GANs using Pytorch involved exploring various architectures and optimizing the adversarial training process. Ensuring stability during training was crucial to generate high-quality synthetic medical data that accurately reflects real-world scenarios.
  3. Validating User-Submitted Data: Robust validation mechanisms were implemented to ensure the integrity of training data. Data format checks, outlier detection, and quality control measures were enforced to verify the correctness and authenticity of data submitted by participating users, preventing potential security risks.
  4. Building the Federated Learning Framework: Developing a customized federated learning framework tailored to project requirements was a significant undertaking. This involved designing communication protocols, aggregation algorithms, and secure data transmission mechanisms to securely aggregate model updates from multiple data sources while preserving data privacy.

Project Resources:


AlchemistAI represents a significant step forward in healthcare AI. By combining generative AI and federated learning, we've laid the groundwork for secure, ethical, and effective AI-driven healthcare solutions. Our project not only addresses current challenges but also paves the way for a more efficient and patient-centric healthcare system.

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