Google Cloud and Microsoft are launching generative AI tools aimed at healthcare by streamlining documentation and searching unstructured data to optimize workflows. Vendors keep rolling out healthcare focused AI and automation, but the industry still struggles with transformation as well as data interoperability.
Google Cloud said it added a feature called Visual Q&A in its Vertex AI Search for healthcare. Visual Q&A is designed to search tables, charts, medical images and diagrams for data. Google Cloud also said Gemini 2.0 models are available in its healthcare offering.
The idea is that physicians will leverage multimodal search and models to get a better view of patient health.
Microsoft launched Microsoft Dragon Copilot, an AI assistant for clinical workflows. The Dragon Copilot will be combined with DAX, a natural language voice dictation platform that includes ambient listening, genAI models and healthcare guardrails. Microsoft acquired Nuance in 2022 for its healthcare foothold and later launched Microsoft Cloud for Healthcare.
According to Microsoft, Dragon Copilot can streamline documentation with multilanguage ambient note creation, surface medical information from trusted sources and automate tasks such as notes, evidence summaries and referral letters. Microsoft's Dragon Copilot can also navigate electronic health records.
The news from Microsoft and Google Cloud are a barrage of releases timed for HIMSS, a large healthcare technology conference.
AI in healthcare: Are we there yet?
At Constellation Research's Ambient Experience Summit 2025, there were multiple talks about healthcare, AI and patient experiences. See: Constellation Research Ambient Experience Summit 2025: 10 CX takeaways on people, data, AI
Joseph Anzalone, VP of Marketing at GE Healthcare, said the company is transforming from a capital equipment company to one focused on AI applications. GE Healthcare is planning to leverage the 96% of unused data from its devices to enhance clinician experiences.
"We're focusing on the clinician experience now and building AI applications on top of foundation models that will give clinicians usable data and predictive analytics," said Anzalone, who added that healthcare workflows are often slowed by data silos across various systems, insurance providers and channels.
The bet is that AI can help overcome legacy infrastructure.
Kimberly Powell, VP of Healthcare at Nvidia, said digital devices, digital biology and clinical will all be big markets for agentic AI systems.
Powell argued that AI agents will serve as a digital workforce due to shortages. AI agents will succeed because they can sit on top of existing infrastructure. She said:
"I believe agentic AI can overlay on the healthcare system unlike technology in the past. Agents can connect and have a foundation model through an API. Since there's an API, AI agents can navigate autonomously over what is otherwise a antiquated and very complex and disaggregated healthcare system."
Also see: BT150 Spotlight: Centurion Health's Johnny Wu on delivering healthcare in prisons
- BT150 Spotlight: MultiCare Health System's Laurie Wheeler on optimization, change management and healthcare transformation
- BioNtech, InstaDeep bet on genAI models to advance R&D, drug discovery, cancer treatment
- AI 15O's Matt Lewis on GenAI adoption, psychology and life sciences
- Welldoc Chief AI Officer Anand Iyer on AI, data and healthcare transformation
- How ResMed’s data prowess sets it up for AI, sleep health market expansion
Data interoperability needed
While these AI visions for healthcare are in progress and impressive, Constellation Research CEO Ray Wang argued in a report that the industry needs to focus on healthcare data interoperability.
Wang said:
"Healthcare systems around the world are facing a confluence of challenges such as aging and declining populations, rising cost of delivery, clinical workforce shortages, and increasing expectations for better outcomes at lower prices. Although healthcare systems have accelerated their digitization projects in an effort to modernize, the results have not shown massive efficiency. One of the biggest opportunities to accelerate digitization and prepare for the Age of AI is improved healthcare data interoperability. In fact, interoperability will play a significant role in driving down costs and improving patient outcomes."
The industry needs to focus on interoperability because "current patients, physicians, and healthcare providers and payors face a constant struggle to integrate, manage, and orchestrate across a hodgepodge of physical and digital functional fiefdoms and silos."
Ultimately, the healthcare industry needs to focus on vendors that support data interoperability, hone data consistency, make interoperability part of governance and leverage standards.
