Bell Eapen MD, PhD.

Bringing Digital health & Gen AI research to life!

About Me

Translational research in health IT is my passion.

Bell Eapen MD, PhD.

Digital Health & Gen AI Consultant
I am Bellraj (Bell) Eapen, a techie MD (dermatologist) with a PhD in Information Systems from McMaster University. As an R&D lead engineer at Mayo clinic, I specialize in cloud architecture for Gen AI and multimodal machine learning using FHIR.

I maintain several software libraries on GitHub. I facilitate the adoption of Generative AI and FHIR in healthcare organizations. As a consultant, I deliver prototypes & models instead of reports and recommendations!

Checkout my GitHub repo and Contact Me for your next project!


Things that I work on.

Deploy healthcare machine learning pipelines on the cloud

Facilitate ML and AI research in healthcare

Chatbot & Conversational AI for clinical workflows

Design AI applications that fits enterprise architecture

Data warehousing and health data analytics for healthcare (FHIR & OHDSI OMOP)

Customize OSCAR, OpenMRS, DHIS2 and RedCap


Things that I'm good at.



My thoughts.

Bovee and Thill, CC BY 2.0 , via Wikimedia Commons

Come, join us to make generative AI in healthcare more accessible! 

ChatGPT captured the imagination of the healthcare world though it led to the rather misguided belief that all it needs is a chatbot application that can make API calls. A more realistic and practical way to leverage generative AI in healthcare is to focus on specific problems that can benefit from its ability to synthesize […]

Why is RAG not suitable for all Generative AI applications in healthcare?

Why is RAG not suitable for all Generative AI applications in healthcare?

Retrieval-augmented generation (RAG) is a method of generating natural language that leverages external knowledge sources, such as large-scale text corpora. RAG first retrieves a set of relevant documents for a given input query or context and then uses these documents as additional input for a neural language model that generates the output text. RAG aims to improve the factual accuracy, diversity, […]

Grounding vs RAG in Healthcare Applications 

Grounding vs RAG in Healthcare Applications 

Both Grounding and RAG (Retrieval-Augmented Generation) play significant roles in enhancing LLMs capabilities and effectiveness and reducing hallucinations. In this post, I delve into the subtle differences between RAG and grounding, exploring their use in generative AI applications in healthcare.  What is RAG?  RAG, short for Retrieval-Augmented Generation, represents a paradigm shift in the field […]

Mind map of LLM techniques, methods and tools

To or not to LangChain

LangChain is a free and accessible coordination framework for building applications that rely on large language models (LLMs). Although it is widely used, it sometimes receives critiques such as being complex, insecure, unscalable, and hard to maintain. As a novel framework, some of these critiques might be valid, but they might also be a strategy […]

Translational Research in Digital Health and Gen AI 

Translational Research in Digital Health and Gen AI 

Translational research is the process of turning scientific discoveries into practical applications that can benefit society. It involves bridging the gap between different stages of research, from basic to applied, and between different stakeholders, such as researchers, clinicians, policy makers, and industry. Translational research aims to accelerate the transfer of knowledge and technology from the […]

LLM notations and symbols

Architecting LLM solutions for healthcare – Part II

Healthcare data and applications are complex and require careful design choices. In a previous post, I have outlined some examples and tools for architecting LLM solutions. One challenge of developing LLM applications for healthcare is the complexity and diversity of the architectures involved. LLMs can be used for different purposes, such as information retrieval, text […]