Close

New AI Research From CDW

See how IT leaders are tackling AI opportunities and challenges.

Jul 02 2025
Artificial Intelligence

Surprising Ways AI Is Transforming Care Delivery

Artificial intelligence experts discussed unique use cases for AI in healthcare at the recent AWS Summit in Washington, D.C.

Ambient documentation is touted by many healthcare IT and clinical leaders as being an artificial intelligence use case that can make a great impact on clinician workloads without much disruption. However, AI’s usefulness in healthcare today goes beyond ambient listening.

At the AWS Summit Washington, DC 2025, healthcare AI leaders discussed ways they are using AI and AI tools offered by Amazon Web Services to improve patient outcomes and clinical workflow efficiency.

Click the banner below to read the new CDW Artificial Intelligence Research Report.

 

Generative AI Helps Kids Manage Their Diabetes

Diabetes can lead to complications such as amputations, blindness and heart attacks if blood sugar is not managed properly. For young children with type 1 diabetes, a lifelong condition, it’s important to teach them to understand and manage their condition early.

Steven Silvers, game developer and research assistant at Harvard University, helped to develop a serious game, a video game designed for education purposes, focused on creating a more accessible and personalized learning experience for children with type 1 diabetes. The T1D Learning Camp game allows the child to converse, play and explore.

In the conversational sections, the child interacts with game characters to have real conversations powered by generative AI, according to Silvers. The developers manually mapped out hundreds of thousands of conversation pathways so the AI can create tailored responses in real time. First, the child goes through a preprogrammed section of the game to learn a new concept, such as which foods provide a slow, steady rise in blood sugar and which create a spike. Then, generated AI conversations occur after those lessons to check the child’s understanding of the concept and how it relates to their daily life. The children can also talk about their experiences with in-game characters.

READ MORE: Generative AI help clinicians interpret ABG test results.

Silvers explained that the game was created with Godot, an open-source game engine, and connects with Amazon Bedrock to support the generative AI functionality. According to an AWS article about the game, “To manually start the connection, Amazon API Gateway is configured to call an AWS Lambda function using Python to invoke Amazon Bedrock APIs and return results to Godot.”

Because young children cannot read and type, the game relies on speech to text and text to speech. Amazon Polly and Amazon Transcribe facilitate the game’s speaking and listening functionality while Amazon Translate makes the game accessible to children who don’t speak English, Silvers explained.

The game also leverages Amazon Titan with Guardrails and the Amazon Titan Image Generator foundation model to personalize the game. Paired with these tools, Amazon Nova Canvas and retrieval augmented generation, the game is able to understand different cultures and their diets. It can create food images in a playful style similar to that used in the game, based on the child’s culture and diet.

“These are ways AI can be used to create a more accessible experience. It’s a more fun and effective way to teach children how to manage their blood sugar that can lead to healthier and happier lives free of diabetes complications,” said Silvers.

EXPLORE: Prepare data for AI implementation in healthcare.

Hybrid AI-Powered Search Engine Improves Data Accessibility

Physicians are often battling with electronic health records in their attempts to find relevant information, said Dr. Dinesh Rai, clinical AI engineer at the Innovation and Digital Health Accelerator at Boston Children’s Hospital.

Many EHRs allow only a certain number of search parameters, and information stored in data lakes isn’t accessible at the bedside. Rai and his team’s goal was to allow the most advanced physicians caring for the most complicated patients to search historic data to get a better idea of what journey their patient may be on and how to best treat them.

To do this, the team turned to AI-powered hybrid search.

“We run through a whole series of steps to take a query from a physician and create an object that can be used in the search,” said Rai.

Creating patient cohorts is a major part of the process. Finding patients based on certain inclusion and exclusion criteria used to be a manual process, but Rai and his team have been working to automate it to make the search quicker and more accurate. Another benefit of AI-powered hybrid search is that it can provide insights on patients in clinic and the ER, expanding the search to the full patient population rather than just one section of patients.

Click the banner below to find out how to take advantage of data and AI for better healthcare outcomes.

 

With this tool, physicians can make decisions rather than relying on guesses or hunches, said Rai. “They can know what worked in the past and what didn’t, especially for niche patients.”

People looking for that kind of data can come to Rai’s team with the medical records, and the team will iterate, page by page, to store structural data in a SQL data base. They normalize numerical data to help the algorithm search better and then use semantic tagging and name recognition to label records with information about the disease and what happened. The team takes unstructured data and turns it into vectors to improve search.

The team creates different data sets on an AWS instance and stores them so their AI agent can search through the data. The AI agent has knowledge about what the data looks like, what’s in the SQL tables, what’s in the vector database and what types of tags are used, Rai said.

“When a user inputs a query, the agent takes that natural-language query and transforms it into an object to search the SQL database,” he said.

If a physician is looking for complications related to diabetes, that object will be used to search SQL data, vectors and the semantic database. It will pull out patients by filters and output a list of patients that fulfill the query based on which are the closest and farthest. When at the bedside, a physician can find out what has happened to past patients with the same disease to determine what their trajectory could be based on the intervention the doctor is considering.

UP NEXT: Use data to improve clinical workflows and optimize the EHR.

Don Wu/Getty Images