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The Impact of Conversational AI on Healthcare Outcomes and Patient Satisfaction

JustCall

The digital revolution has left an imprint on the healthcare industry as well. with healthcare. with healthcare. Here, we talk about the impact of artificial intelligence on healthcare – more specifically, from a conversational AI angle. What is Conversational AI in Healthcare?

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Large language model inference over confidential data using AWS Nitro Enclaves

AWS Machine Learning

Leidos is a Fortune 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. In our example use case, an LLM service is designed to answer employee healthcare benefit questions or provide a personal retirement plan.

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Exploring summarization options for Healthcare with Amazon SageMaker

AWS Machine Learning

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. In a healthcare setting, this would mean giving the model some data including phrases and terminology pertaining specifically to patient care.

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Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

AWS Machine Learning

It enables you to privately customize the FMs with your data using techniques such as fine-tuning, prompt engineering, and Retrieval Augmented Generation (RAG), and build agents that run tasks using your enterprise systems and data sources while complying with security and privacy requirements.

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What is a Key Differentiator of Conversational AI?

Solvvy

Chatbots help you meet this demand by allowing your customers to type or ask a question and get an answer immediately. Although some chatbots are rules-based and only enable users to click a button and choose from predefined options, other solutions are AI chatbots. What is Conversational AI? Works around the clock.

Chatbots 119
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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning

RAG allows models to tap into vast knowledge bases and deliver human-like dialogue for applications like chatbots and enterprise search assistants. Prerequisites For this example, you need an AWS account with a SageMaker domain and appropriate AWS Identity and Access Management (IAM) permissions.

APIs 104