AWS Machine Learning Blog
Category: Healthcare
How healthcare payers and plans can empower members with generative AI
In this post, we discuss how generative artificial intelligence (AI) can help health insurance plan members get the information they need. The solution presented in this post not only enhances the member experience by providing a more intuitive and user-friendly interface, but also has the potential to reduce call volumes and operational costs for healthcare payers and plans.
Medical content creation in the age of generative AI
Generative AI and transformer-based large language models (LLMs) have been in the top headlines recently. These models demonstrate impressive performance in question answering, text summarization, code, and text generation. Today, LLMs are being used in real settings by companies, including the heavily-regulated healthcare and life sciences industry (HCLS). The use cases can range from medical […]
Evaluation of generative AI techniques for clinical report summarization
In this post, we provide a comparison of results obtained by two such techniques: zero-shot and few-shot prompting. We also explore the utility of the RAG prompt engineering technique as it applies to the task of summarization.
Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline.
Deploy large language models for a healthtech use case on Amazon SageMaker
In this post, we show how to develop an ML-driven solution using Amazon SageMaker for detecting adverse events using the publicly available Adverse Drug Reaction Dataset on Hugging Face. In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried.
How HSR.health is limiting risks of disease spillover from animals to humans using Amazon SageMaker geospatial capabilities
This is a guest post co-authored by Ajay K Gupta, Jean Felipe Teotonio and Paul A Churchyard from HSR.health. HSR.health is a geospatial health risk analytics firm whose vision is that global health challenges are solvable through human ingenuity and the focused and accurate application of data analytics. In this post, we present one approach […]
Learn how Amazon Pharmacy created their LLM-based chat-bot using Amazon SageMaker
Amazon Pharmacy is a full-service pharmacy on Amazon.com that offers transparent pricing, clinical and customer support, and free delivery right to your door. Customer care agents play a crucial role in quickly and accurately retrieving information related to pharmacy information, including prescription clarifications and transfer status, order and dispensing details, and patient profile information, in […]
Create an HCLS document summarization application with Falcon using Amazon SageMaker JumpStart
Healthcare and life sciences (HCLS) customers are adopting generative AI as a tool to get more from their data. Use cases include document summarization to help readers focus on key points of a document and transforming unstructured text into standardized formats to highlight important attributes. With unique data formats and strict regulatory requirements, customers are […]
Automate prior authorization using CRD with CDS Hooks and AWS HealthLake
Prior authorization is a crucial process in healthcare that involves the approval of medical treatments or procedures before they are carried out. This process is necessary to ensure that patients receive the right care and that healthcare providers are following the correct procedures. However, prior authorization can be a time-consuming and complex process that requires […]
Exploring summarization options for Healthcare with Amazon SageMaker
In today’s rapidly evolving healthcare landscape, doctors are faced with vast amounts of clinical data from various sources, such as caregiver notes, electronic health records, and imaging reports. This wealth of information, while essential for patient care, can also be overwhelming and time-consuming for medical professionals to sift through and analyze. Efficiently summarizing and extracting […]
Transform, analyze, and discover insights from unstructured healthcare data using Amazon HealthLake
Healthcare data is complex and siloed, and exists in various formats. An estimated 80% of data within organizations is considered to be unstructured or “dark” data that is locked inside text, emails, PDFs, and scanned documents. This data is difficult to interpret or analyze programmatically and limits how organizations can derive insights from it and […]