A large language model (LLM) is an artificial intelligence model designed to understand and generate human-like text based on vast amounts of training data.
With rapid evolution of Generative AI, to embrace it and help us improve productivity is a must. Let's discuss and embrace the ideas of how we can leverage Generative AI to improve our routine work.
Our next Developer Meetup will take place on July 26, 17:30 pm at the CIC Venture Café in Cambridge.
Join us to learn Generative AI Use Cases + Reference Architecture in Healthcare, witness the demo of LLMs in Healthcare and share your thoughts on the topic.
Previous post - Using AI to Simplify Clinical Documents Storage, Retrieval and Search
This post explores the potential of OpenAI's advanced language models to revolutionize healthcare through AI-powered transcription and summarization. We will delve into the process of leveraging OpenAI's cutting-edge APIs to convert audio recordings into written transcripts and employ natural language processing algorithms to extract crucial insights for generating concise summaries.
The continuing convergence of AI technologies and healthcare systems has brought forward many compelling advancements. Let's set the scene. If you have interacted with dynamic models like ChatGPT, you might have, like many of us, begun to envision its application using your unique datasets. Suppose in the healthcare sector you wish to link this technology with Electronic Health Records (EHR) or Electronic Medical Records (EMR), or perhaps you aim for heightened interoperability using FHIR's resources.
This project is an experiment to use OpenAI API to answer to user prompts in the healthcare domain using FHIR resources and Python code.
Generative AIs, like the LLM models available on OpenAI, has been demonstrated remarkable power to understand and answer high level questions. They use huge amounts of data to train their models, so they can answer complex questions.
They can even use programming languages to create code based on prompts - and I have to confess that the idea of getting my work automated causes me some anxiety.
In a fast-paced clinical environment, where quick decision-making is crucial, the lack of streamlined document storage and access systems poses several obstacles. While storage solutions for documents exist (e.g, FHIR), accessing and effectively searching for specific patient data within those documents meaningfully can be a significant challenge.
AI has made document search remarkably powerful. Question and answering over docs has never been easier with open-source tools like Chroma and Langchain to store and use vector embeddings to query across Generative AI APIs.
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As an AI language model, ChatGPT is capable of performing a variety of tasks like language translation, writing songs, answering research questions, and even generating computer code. With its impressive abilities, ChatGPT has quickly become a popular tool for various applications, from chatbots to content creation.
But despite its advanced capabilities, ChatGPT is not able to access your personal data. So in this article, I will demonstrate below steps to build custom ChatGPT AI by using LangChain Framework:
Demonstration example for the current Grand Prix contest for use of a more complex Parameter template to test the AI.
There is documentation. A recruitment consultant wants to quickly challenge candidates with some relevant technical questions to a role.
Can they automate making a list of questions and answers from the available documentation?
One of the most effective ways to cement new facts into accessible long term memory is with phased recall.
Posing a question to consider during the current Grand Prix competition.
I wanted to share an observation about using PDFs with LangChain.
When loading the text out of a PDF, I noticed there was an artifact of gaps within some of the words extracted.
For example (highlighted in red)
Keywords: ChatGPT, COS, Lookup Table, IRIS, AI
Here is another quick note before we move on to GPT-4 assisted automation journey. Below are some "little" helps ChatGPT had already been offering, here and there, during daily works.
And what could be the perceived gaps, risks and traps to LLMs assisted automation, if you happen to explore this path too. I'd also love to hear anyone's use cases and experiences on this front too.
One of the simplest tasks could be Lookup tables.
Large language models are stirring up some phenomena in recent months. So inevitably I was playing ChatGPT too over last weekend, to probe whether it would be a complimentary to some BERT based "traditional" AI chatbots I was knocking up, or rather would it simply sweep them away.
A thought comes to mind while playing. By going slightly theoretical or philosophical, eventually interoperability standards such as HL7 and FHIR etc are kind of "languages", right? HL7 has its own grammar, rules, vocabulary and even dialects - every system speaks its own tone.