Tweaking Medicine with Large Language Models (LLM)

Here’s some selective information on the use of LLM, AI Frameworks, RAG, useful for modern Healthcare app development.

Large language models (LLMs) are a type of generative AI that are often used interchangeably with “foundation models”. LLMs are AI programs that can perform a variety of natural language processing (NLP) tasks by using deep learning techniques and large data sets to understand, summarize, generate, and predict new content.

LLMs can be used in many fields, including tech, healthcare, science, customer service, marketing, legal, banking, Text generation, Translation, summarization of content, rewriting content, classification and categorization, sentiment analysis, and conversational AI and chatbots.

Large Language Models are used in healthcare for medical writing, education, and research management. Here are the actual use cases, programs built using this technology:

How do LLM’s work in healthcare?

Healthcare chatbot apps – Florence Chatbot and Babylon Health Chatbot, currently being used by the NHS in the UK have implemented LLM’s. ClinicianCompanion by MIT, uses LLMs to provide recommendations based on patient data. GatoTron LLM, from the NIH, examines EHRs for potential drug interactions and adverse events.

• Large language models improve patient care, medical research, and healthcare systems. These analyze patient data, medical literature, and guidelines to provide real-time insights for diagnosis, treatment plans, and monitoring.
• They automate the generation of healthcare documents, such as consent forms, waivers, and discharge summaries, by extracting relevant information from patient records and pre-populating the documents.
• LLMs analyze patient data and provide real-time insights. They assist patients in understanding the real-world implications of their diagnosis.
• This saves time, reduces the risk of errors, and makes patients feel like they know the provider.
• These recognize the context in which words are used, allowing them to more accurately interpret patient conversations and manage a person’s health condition.

LLM enabled chatbots assisting doctors in making informed decisions. They seldom get tired and are active round the clock. They help in checking symptoms, manage medications, and monitor chronic health problems. Such bots are also being used in appointment bookings, prescription renewals, and insurance confirmations – assisting healthcare professionals focus on patient care. They handle large volumes of patients with accuracy.

Which AI framework do large language models use?

Mobile app development companies are increasingly implementing AI frameworks to use Large language models and neural network architecture to generate content very close to human generated content. Example AI frameworks that LLMs use are: OpenAI, Caffe, Apache MXNet, Amazon SageMaker, Retrieval-Augmented Generation,  Fusion-in-Decoder, Retrieval-Augmented Language Model, Text-To-Text Transfer Transformer with Retrieval, Knowledge-intensive Language Tasks, Dense Passage Retrieval with Generative Models, BERT Summarizer with Retrieval, Chain-of-Thought Models with Retrieval, Generative Retrieval-Optimized Transformer, Multimodal Augmented Retrieval for Generative Models, and likewise.

What is the significance of tagging Retrieval-Augmented Generation with large language models?

Retrieval-augmented generation combines large language models with information retrieval systems to improve the output. It does not connect it to an external knowledge database or reference authoritative information before generating a response. This process helps LLMs overcome limitations of parametric memory, providing more accurate, up-to-date, and relevant responses. The use of RAG improves output of LLM to be more accurate, up to date, helpful in project management, risk assessment and decision making.

RAG primarily consists of retriever (to recover relevant data from indexed dataset based on user’s query) and generator (to augment the retrieved data by framing it within a prompt context and inputting it to LLM to get desired output)

But before implementing RAG, they need to be tweaked for open-domain, consumer settings, closed domain, enterprise settings, customer care chatbots to improve employee experience by integrating with internal databases and documents to provide accurate answers to questions about company operations, benefits and more.

How does Retrieval-Augmented Generation enhance the efficiency of Large Language Models?

RAG overcomes the limitations of Large Language Models parametric memory by allowing them to access real-time data, which improves contextualization and provides up-to-date responses. It makes AI-generated content more transparent by allowing it to cite sources. It comes with updatable memory that eliminates the need for frequent model retraining, making it a cost-effective solution. RAG implementation greatly enhances natural language processing tasks. It integrates external knowledge in real time to generate more accurate and informed answers. AI development companies make use of RAG to implement search algorithms to query external data  – knowledge base, web pages, and databases – and incorporate the pre-processed information into the pre-trained large language models.