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AI Product for Medical Affairs Analytics

In collaboration with AstraZeneca, we developed a cutting-edge AI product designed to revolutionize Medical Affairs analytics. Our solution automates the analysis of CRM records for medical science liaisons, significantly enhancing efficiency in the Healthcare & Pharma sector. Additionally, we deployed advanced large language models to provide comprehensive overviews and actionable insights into large-scale Medical Affairs operations. This AI-driven approach empowers AstraZeneca to make data-driven decisions with greater speed and precision, optimizing both their commercial and operational strategies.

Testimonial

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Challenge

  • Automating CRM Record Analysis: Developing an AI solution capable of accurately automating the analysis of extensive CRM records managed by medical science liaisons. This required the AI to handle large volumes of complex data while ensuring high accuracy and relevance in its analysis.

  • Deploying Large Language Models for Medical Affairs: Implementing large language models that could effectively process and interpret vast amounts of data related to Medical Affairs. The challenge was to ensure these models provided actionable insights that could directly inform strategic decisions in a highly regulated industry.

  • Ensuring Data Privacy and Compliance: Developing the AI solution in a way that strictly adhered to healthcare industry regulations and data privacy standards, especially when handling sensitive patient and research data.

  • Scalability and Adaptability: Designing an AI solution that could scale with AstraZeneca’s growing data needs and adapt to future advancements in AI technology, ensuring long-term value and flexibility in a dynamic industry.

  • User Adoption and Training: Ensuring that the medical science liaisons and commercial operations teams could effectively adopt and utilize the new AI tools, requiring the development of intuitive interfaces and comprehensive training programs.

Solution

Technologies

  • ChatGPT: Used as a core large language model (LLM) to automate the analysis of CRM records, providing natural language processing capabilities that generate accurate and contextually relevant insights for Medical Affairs operations.

  • Llama-2: Integrated alongside ChatGPT, Llama-2 was fine-tuned for specific Medical Affairs applications, enhancing the AI system's ability to interpret complex data and deliver actionable insights tailored to AstraZeneca's needs.

  • Google Cloud: Deployed the AI solution on Google Cloud, leveraging its scalable and secure infrastructure to handle large-scale data processing and ensure reliable, high-performance operation of the AI models.

  • Machine Learning Frameworks: Utilized state-of-the-art machine learning frameworks such as TensorFlow and PyTorch to develop, fine-tune, and optimize the AI models, ensuring they deliver high accuracy and performance.

  • Custom AI Model Development: We leveraged both ChatGPT and Llama-2 large language models (LLMs) to create a powerful AI system capable of automating the analysis of CRM records for medical science liaisons. These models were fine-tuned to handle the specific data types and complexities of Medical Affairs, ensuring high accuracy and relevance in the insights generated.

  • Deployment on Google Cloud: The AI solution was deployed on Google Cloud, providing a robust and scalable infrastructure that supported the processing and analysis of large volumes of data. Google Cloud’s powerful computing resources ensured that the AI models operated efficiently, even under heavy data loads.

  • Advanced Data Privacy and Compliance Measures: The AI solution was built with stringent data privacy protocols and compliance measures, ensuring that all processes adhered to healthcare industry regulations. This included secure data handling practices, encryption, and access controls to protect sensitive patient and research information.

  • Scalable and Adaptable Architecture: We developed the AI system with scalability in mind, allowing it to grow with AstraZeneca’s data needs. The architecture was also designed to be adaptable, enabling easy updates and the incorporation of new AI advancements as they become available.

"They have a phenomenal knowledge of AI."


Serhii Myroshnychenko, Director, Bio-Pharma Medical Evidence

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