GE Healthcare has been working on machine learning, deep learning and artificial intelligence for years, but now the company sees an inflection point where generative AI can transform healthcare from products to workflow to efficiencies that improve the customer experience.

Parminder Bhatia, Chief AI Officer of GE Healthcare, said the emergence of multimodal large language models (LLMs) can uniquely improve healthcare, which is built on everything from different modalities, imaging data, clinical notes, voice interaction, electronic health records and other data.

Before GE Healthcare, Bhatia oversaw generative AI and large language models at Amazon Web Services. His group worked on Amazon Q and Amazon Bedrock. GE Healthcare and AWS recently announced a partnership to transform healthcare with a focus on purpose-built generative AI models using services such as Bedrock.

We caught up with Bhatia, an AI 150 inductee, at Constellation Research's AI Forum in New York to talk shop. Here's a look at the takeaways.

GE Healthcare's approach to AI. Bhatia has been in his current role for about 18 months overseeing the strategy and vision for AI at GE Healthcare. For GE Healthcare, the AI strategy revolves around the AI going into the MRI, CT and X-ray machines as well as digital platforms that focus on clinical and operational efficiencies across a hospital.

"There's a lot of focus on how we build these technologies that can really streamline workflow," said Bhatia. For instance, AI in an MRI machine that can reduce scan time by 50% with the same quality doubles the efficiency and productivity of the workforce.

Other examples of AI's role at GE Healthcare include AI in ultrasound equipment that can act as a copilot, remote scans and imaging and technologies that "improve the efficiencies and accelerate getting better diagnosis, solving problems in treatment and cancer areas as well," said Bhatia.

GE Healthcare has been a pioneer within machine learning and deep learning for more than a decade and has the highest number of FDA approved app authorizations three years in a row.

Why generative AI and healthcare go together. Bhatia said LLMs have been all the talk, but the excitement around them is that they are multimodal. That ability to be multimodal means they apply well to healthcare.

He said:

"These technologies are truly multimodal in nature and that means they're more tailored for healthcare, which consists of data coming from different modalities, imaging data, clinical notes, voice interaction, your EHRs and other data. As these technologies were being built out it made sense for me to get back into healthcare. It's the perfect opportunity to apply these applications."

Patient experience and AI. Bhatia said AI will ultimately have an impact on the patient experience as workflows and staffing levels are improved for diagnosis to screening to treatment and therapy. GE Healthcare Command Center is using AI to streamline hospital operations, manage staffing and send triggers for actions. While many of those technologies don't affect the patient directly, the patient experience is improved with capacity planning.

"These technologies streamline operations and that becomes relevant across a spectrum of things," said Bhatia. "Patient guidance will also be key as we take care from inside the hospital to outside with patient monitoring and virtual care at home."

These hospital workflows will give a longitudinal patient view across care that improves experiences, he said.

Indeed, GE Healthcare recently acquired MIM Software, a company that manages workflows from diagnosis to treatment and therapy. A few recent developments with MIM include:

Personalization of care. Bhatia said AI will also play a big role in personalized treatment for cancer that deliver targeted radiation to kill cells.

"In the next three to five years, you're going to have thousands of variations in which these different radiopharmaceutical drugs can be given to the individual patients," he said. "MIM Software is designed to address the complexities that happen across the system, where it provides solutions to navigate the expanding landscape of personalized treatment."

Bhatia added:

"A lot of these things are starting with operational efficiency, but also combining multimodal data. I think that's where AI is becoming a key enabler, not just at the diagnosis level, but health clinicians can streamline the longitudinal view of the patient's data, which is truly multimodal. That technology and data can really streamline the operations, which has impact on better therapy and more personalized therapy for patients as well."

GE Healthcare's approach to AI. Bhatia said the company is taking a hybrid approach to AI and investing in talent focused on cloud and AI. "We are bringing a lot of that muscle for cloud and AI across the spectrum," he said. "That becomes the key component as we're looking into a lot of problems and challenges as well."

The hybrid strategy will mean "a lot of things happen on prem and a lot of things will happen in the cloud to accelerate and transform," said Bhatia. With AWS, GE Healthcare will look to building its own foundational models as well as using multiple LLMs for everything from workflows to equipment to treatments and imaging. Bhatia said:

"The partnership we announced with AWS is about strategy and foundational model building for building our own proprietary genAI, streamlining workflows and developing use cases. The partnership is really 1+1 is greater than 2 because you get a lot of benefits from security and scale with AWS and GE Healthcare being in more than 160 countries."

This approach to hybrid AI will also mean multiple partnerships for clinical research. Ultimately, GE Healthcare wants to be able to predict if a patient is going to skip or arrive late to appointments adapt workflows, and build in flexibility, said Bhatia.'

Model choice. Bhatia said flexibility with foundational models is critical. "One model is not going to solve all problems and you'll have to look to the clinical side and the operational side of things," he said. "The first place AI can have an impact is to alleviate cognitive and data overload to highlight what's relevant."

Bhatia added that models will also need to be adapted and used for specific use cases. Open-source models also have potential to be adapted for a specific use cases.

AI as a horizontal and vertical tool. Bhatia said it's important for AI leaders to think about generative AI as a horizontal enabler and a technology that can be used to drill down in specific areas. He said:

"You can build these AI algorithms for breast cancer, but they can easily be adapted to prostate cancer or lung cancer. And I think that's where these technologies are becoming really game changer. How do you adapt them, not just looking into the vertical side of things, going from diagnosis to treatment therapy and the entire patient journey, but also how they can be adapted across the spectrum?"

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Data to Decisions, Next-Generation Customer Experience

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#healthcare #cio, #AI #artificialintelligence #machinelearning #ML #generativeAI