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!

Services

Things that I work on.

Cloud Architecture for ML

Deploy healthcare machine learning pipelines on the cloud

ML & AI Research in Healthcare

Facilitate ML and AI research in healthcare

Generative AI

Chatbot & Conversational AI for clinical workflows

AI Application Design

Design AI applications that fits enterprise architecture

FHIR & Analytics

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

Customize Health Information Systems

Customize OSCAR, OpenMRS, DHIS2 and RedCap

Skills

Things that I'm good at.

FHIR
90%
GCP
80%
AI
90%
JAVA
80%
Python
70%
OSCAR EMR
100%
DERMATOLOGY
100%
TensorFlow
85%

Blog

My thoughts.

R&D and Innovation in IT; to or not to combine both

R&D and Innovation in IT; to or not to combine both

- November 15, 2024

R&D and innovation are two related but distinct concepts. My aim is not to delve into the subtle semantic differences between the two but to explore, as an information systems researcher, some organizational factors that may impact individual innovators. My focus is exclusively on information technology and information systems innovation within a corporate setting.  In […]

Ollama

Locally hosted LLMs

- July 14, 2024

TL; DR: From my personal experiments (on an 8-year-old, i5 laptop with 16 GB RAM), locally hosted LLMs are extremely useful for many tasks that do not require much model-captured knowledge.  Image credit: I, Luc Viatour, CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0, via Wikimedia Commons The era of relying solely on large language models (LLMs) for all-encompassing […]

Bovee and Thill, CC BY 2.0 , via Wikimedia Commons

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

- June 15, 2024

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?

- May 11, 2024

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 

- May 4, 2024

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 […]

To or not to LangChain

To or not to LangChain

- April 27, 2024

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 […]