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3M And Amazon To Use Generative AI To Improve Healthcare Documentation



3M And Amazon To Use Generative AI To Improve Healthcare Documentation

(CTN News) – It was announced late last week that two of the world’s most well-known companies, Amazon and 3M, had partnered together to tackle one of the most pressing pain points for healthcare providers: clinical documentation.

Essentially, through this partnership, 3M Health Information Systems (HIS) will work with Amazon Web Services (AWS) to assist them in developing their M*Modal Ambient Intelligence service.

It is explained by the company as a tool that uses “conversational artificial intelligence (AI) and ambient intelligence to automate clinical documentation automatically, so as to transform documentation from a separate, burdensome task for the physician to one that takes place as part of the patient encounter.”

With this partnership, Amazon will be contributing a significant amount of its expertise and resources, most notably through the use of its Amazon Bedrock, Amazon Comprehend Medical, and Amazon Transcribe Medical platforms.

In other words, Bedrock is Amazon’s fresh approach to “build and scale generative AI applications using foundation models (FMs)” and provides the users with the ability to customize FMs with their own data and deploy them as tailored applications based on their own requirements.

There are many examples of these applications, including chatbots, search engines, image generation services, text summarization tools, etc.

There is no doubt that Amazon’s Bedrock is a part of a growing landscape of technology companies launching their own software tools designed with generative AI, most recently by Microsoft and Google.

It is worth mentioning that Amazon has a significant amount of experience in the medical transcription and clinical documentation space thanks to its Transcribe Medical and Comprehend Medical platforms.

As the name implies, the former is a service offering automatic speech recognition (ASR) that allows you to easily add medical speech-to-text capabilities to [voice-enabled applications]; the latter is a natural language processing (NLP) service that uses machine learning to extract medical information from unstructured medical texts such as doctor’s notes, clinical trial reports, or radiology reports.

A combination of these various services and platforms could potentially revolutionize the clinical documentation process that we have today if the best features of each platform are combined.

There is no doubt that these companies have a lot of work ahead of them in order to fully understand the potential of generative AI and how it may unlock an entirely new era of efficiency in healthcare by harnessing its power.

It is important to keep in mind, as with all other technologies, that caution must be taken during the development process-especially when it involves sensitive and private information about patient health.

The healthcare industry may be transformed if, however, this technology is developed in a manner that is ethical, safe, and sound.


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