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Delivering responsible AI in the healthcare and life sciences industry

IBM Journey to AI blog

There is a high likelihood that historically underserved communities may use a generative transformer, especially one that is embedded unknowingly into a search engine, to ask for medical advice. How can we proactively invest in AI for more equitable and trustworthy outcomes? The NIH further stated that between 47.5 million and 51.6

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This AI understands doctor’s notes: Truveta’s new model finds meaning in messy healthcare data

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Truveta Photos) Healthcare data holds great potential to improve medicine, but mining it is not easy. To get to the gold, Truveta built a large AI-powered model to crunch through medical texts from more than 20,000 clinics and 700 hospitals. Truveta chief technology officer Jay Nanduri (left) and CEO Terry Myerson.

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Synthetic data generation: Building trust by ensuring privacy and quality

IBM Journey to AI blog

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together

AWS Machine Learning Blog

Amazon Bedrock is the best place to build and scale generative AI applications with large language models (LLM) and other foundation models (FMs). It enables customers to leverage a variety of high-performing FMs, such as the Claude family of models by Anthropic, to build custom generative AI applications.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning Blog

Amazon Bedrock provides a broad range of models from Amazon and third-party providers, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, embedding, chat, high-level agents with reasoning and orchestration, and more.

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Deploy large language models for a healthtech use case on Amazon SageMaker

AWS Machine Learning Blog

In 2021, the pharmaceutical industry generated $550 billion in US revenue. Overall, $384 billion is projected as the cost of pharmacovigilance activities to the overall healthcare industry by 2022. These events can be reported anywhere, from hospitals or at home, and must be responsibly and efficiently monitored.

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning Blog

A model developer typically starts to work in an individual ML development environment within Amazon SageMaker. In this post, we discuss how the AWS AI/ML team collaborated with the Merck Human Health IT MLOps team to build a solution that uses an automated workflow for ML model approval and promotion with human intervention in the middle.

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