AI in Action: Boosting daily efficiency and unlocking healthcare opportunities

Watch this video on streamlining your workflow with AI tools and how to choose them.

Anya Derbakova, a solutions architect with a passion for healthcare, sat down with the All Things Open team and shared her thoughts on how AI is changing the game for developers.

In her daily work, Anya uses AI tools like ChatGPT to streamline the research process and help quickly pull relevant information from large datasets. For client management, her team uses AI-powered internal tools for summarizing meeting notes and tracking client progress across multiple projects.

AI tools for developers: LangChain & Streamlit

Anya shared two AI tools that have been particularly helpful in her work:

• LangChain: A powerful framework for orchestrating more specific workflows in generative AI. It’s especially useful when building complex applications that require detailed control over how models interact with data.
• Streamlit: A Python package that makes it easy to build slick AI demos and interactive front-end apps. It’s fast, easy to use, and perfect for creating polished AI interfaces quickly — whether for chatbots or any other AI-driven projects.

When choosing AI tools, Anya stresses the importance of understanding your domain and data. For example, healthcare data often requires compliance with regulations (like HIPAA), and some models may not be suitable for handling sensitive info. It’s also important to assess whether a tool will use your queries and documents to train future models—especially if privacy is a concern.

For developers just starting with AI, Anya encourages them to explore chatbots and RAG models (retrieval-augmented generation). These are great entry points, and with so many tutorials available, learning the basics of LLMs (large language models) is easier than ever. She also reassures developers that there’s no need to feel behind — AI is still evolving, and there are plenty of opportunities to catch up and dive in.

Anya’s passion for healthcare shines through as she discusses AI’s potential to optimize hospital operations, improve resource allocation, and even match patients with clinical trials more effectively. In healthcare, AI can drive significant improvements in both efficiency and patient care.

Key takeaways

• Evaluate AI tools based on your domain & data: If you’re working with sensitive data, like healthcare or personal info, ensure the tools you’re considering comply with relevant regulations and understand how your data may be used.
• AI tools to explore: LangChain is great for orchestrating AI workflows, while Streamlit is ideal for quickly building impressive front-end demos for AI projects.
• Start simple & don’t feel left behind: If you’re new to AI, start with simple use cases like chatbots or RAG models (retrieval-augmented generation). There’s a lot of room to grow, and plenty of tutorials out there to help you get started.

Conclusion

AI is opening up countless opportunities for developers, whether you’re optimizing workflows in your day-to-day or diving into specialized fields like healthcare. With tools like LangChain and Streamlit, developers can get hands-on with generative AI and start building impactful solutions. It’s a great time to experiment, learn, and innovate — and as Anya emphasizes, it’s never too late to get started. The future of AI is here, and it’s full of potential.

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