AI in Healthcare: Building Trust and Driving Innovation for Better Patient Outcomes

AI in healthcare: Building trust and driving innovation

Building trust in AI: The key to healthcare's digital transformation AI in healthcare is reshaping the industry by tackling administrative burdens and enhancing patient care. As organizations navigate the rapid evolution of artificial intelligence, success depends on trust, governance and ethical implementation. For AI in healthcare to reach its full potential, organizations must first establish strong foundational elements, according to Aashima Gupta (pictured, left), global director of healthcare solutions at Google Cloud. "We all have seen in the past two years that [AI models] have come to the mainstream," she said. "But what we are hearing back from our customers is that it's not ... about having a model. How do you serve those models? Where is the platform approach where [you have the] ability to serve those models, but these models [are] also tripped? How are you going to deploy? How are you going to scale? How are you going to monitor them?" Gupta and Nneka Emegwa, US health and public services at Accenture PLC, spoke with theCUBE's Rebecca Knight at theCUBE's Coverage of Google Cloud at HIMSS25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the importance of trust in AI adoption in healthcare, addressing ethical concerns and the future of AI-driven digital agents in transforming emergency room workflow. (* Disclosure below.) Addressing ethical concerns in AI in healthcare Trust is the cornerstone of AI adoption in healthcare. Without it, even the most sophisticated AI systems will struggle to gain acceptance from healthcare professionals and patients alike, according to Gupta. Establishing AI principles, governance frameworks and rigorous evaluation criteria is key to ensuring AI-driven decisions are reliable and transparent. "I would say [that I] 100% agree [that] healthcare moves at the speed of trust," Gupta said. "And that is first, then performance [and] then the cost. I would say, from Google's perspective, some best practice to share is [that] we established our AI principles back in 2018. For organizations getting into that AI transformation journey, how many of them have the AI principles? How many have the governance processes in place? For us, each project, each product [and] each partnership are then evaluated as a governance framework around that." Beyond governance, AI in healthcare can play a role in identifying and mitigating bias in healthcare data, ensuring that AI-driven insights lead to equitable access to care, according to Emegwa. Beyond governance, AI in healthcare can play a role in identifying and mitigating bias in healthcare data, ensuring that AI-driven insights lead to equitable access to care. Continuous evaluation and oversight are essential to this process. "I would say also that you can use AI to solve AI issues, like the bias and all that," Emegwa said. "If you think of agentic and the agent, you could also build an agent that does that. Maybe it's a critic agent that keeps looking at your process, making sure that things are coming out as expected, and then also suggesting [and] recommending improvements to that, too. It's an ongoing process. It's not a 'build once and let it run.' As things evolve, you have to check your guardrails." The future of AI in healthcare: Smarter systems, better care As AI capabilities advance, the healthcare industry is on the brink of transformative changes, according to Emegwa. One of the most promising developments is the integration of AI-driven digital agents that streamline emergency room workflows and enhance patient care. This capability has the potential to serve as an intelligent digital companion, orchestrating care delivery before patients even arrive at a hospital. "If you think about AI going beyond just being a tool for automation, but now, it's your big digital companion, is this system providing this intelligence, the growth of intelligence?" Emegwa said. "If you think about the emergency room of the future, let's say a patient comes in ... driven to the [operating room] by an autonomous vehicle [or] autonomous ambulance to start with. These digital agents are pulling all this information before the patient arrives from their wearables, pulling their healthcare information, correlating all that [and] checking the OR capacity." For these innovations to succeed, organizations must move beyond scattered AI experiments and implement enterprise-grade solutions, ensuring a strong foundation for AI adoption, according to Gupta. AI models are powerful tools, but their success depends on the infrastructure that supports them. "My message is, think platform ... enterprise-grade privacy and security are table stakes, especially when you talk agents [and] the orchestration of those agents," she said. "Enterprise workflows ... are deterministic ... generative AI is probabilistic. How do you match the two to serve a task? You need a platform to be able to configure all these, and that's where we shine, and this is what our platform offers." Here's the complete video interview, part of SiliconANGLE's and theCUBE's Coverage of Google Cloud at HIMSS25:

Agentic AI is revolutionizing patient outcomes in healthcare

Google Cloud highlights agentic AI's potential to transform healthcare Artificial intelligence agents could be just what the doctor ordered, creating better, faster and more informed patient outcomes. The current wave of agentic AI is poised to change how healthcare operates by speeding up the process of medical discovery, according to Tom Hittinger (pictured, right), healthcare applied AI leader at Deloitte Touche Tohmatsu Ltd. "Being able to make it easier to discover information, discover insights and shape the care that an individual provider is giving to a patient, that has the potential to really just change the way in which we're driving patient outcomes," he said. "Not just giving something that was a legacy recommendation and evidence-based guidelines from 10 years ago, but something that's fresh on the bleeding edge that enables a patient to have information specific to them." Hittinger and Schweta Maniar (left), global director of healthcare and life sciences at Google Cloud, spoke with theCUBE's Rebecca Knight at theCUBE's Coverage of Google Cloud at HIMSS25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the development and application of AI in healthcare. (* Disclosure below.) How agentic AI improves patient outcomes Healthcare companies and medical institutions would benefit from scaling AI sooner rather than later, according to Hittinger and Maniar. Last December, Google Cloud launched AgentSpace, a one-stop shop for agentic AI that can source different types of data, structured or unstructured, to solve the task at hand. "The [companies] who are realizing value are the ones who are not thinking about it from a point solution, but they are thinking about it across their entire enterprise, like a true digital transformation," Maniar said. "We're coming to a point where now, we're having the boards and the leaders of these organizations not just wanting to experiment with these tools and AI, but it's now becoming a fundamental foundation of their strategy moving forward." Agentic AI is a big deal for healthcare because it can transform the entire system, according to Hittinger. An AI agent might educate patients on unfamiliar medical terms, generate code, speed up the process of reviewing a patient's medical history or even scan swathes of medical papers to deliver insights. "AI in the first wave is more AI as an assistant: You prompt it [and] it responds," Hittinger said. "But going from assistant to true collaborator is ... I think, the real difference is how do you leverage these new agentic AI platforms and tools to not just do a single task, but to actually transform an entire process?" Even though AI could have a big impact on healthcare, adoption takes time, and medical institutions have a lot of legacy technology in place. Transforming the whole system will take time, so the first step is proving that the technology works, according to Maniar. "If you look at what are your biggest business problems ... and identify what are the less risky, maybe quicker wins, they might not be as exciting, but you're able to show in a very controlled manner how it actually can apply, and then you start scaling up from there," she said. "That's the biggest advice because you've got these pie in the sky ideas, but sometimes, the highest value opportunities might be the ones that might be perceived as not as exciting." Here's the complete video interview, part of SiliconANGLE's and theCUBE's Coverage of Google Cloud at HIMSS25:

Responsible innovation: The broad view of AI in healthcare

AI's future in healthcare: A balancing act of innovation and responsibility AI in healthcare delivery is already showing considerable promise. However, with higher stakes than ever, concerns mount over data privacy, regulatory compliance, explainability and the human element in artificial intelligence-driven decision-making. To assuage these concerns, enterprises must adopt and implement self-regulation, with internal best practices in effect ahead of external regulations, according to Aashima Gupta (pictured, left), global director of healthcare solutions at Google Cloud. "Enterprise-grade privacy security is table stakes to build that trust," she said. "From the regulation perspective, self-regulation is a necessity. Depending on the use cases, if you are building AI that touches patients [and] clinical diagnostics, we believe regulation will be very important. Our stance is that AI is too important to not be regulated." Gupta and Ramaswamy Rajagopal (right), vice president of healthcare strategy at Cognizant Technology Solutions Corp., spoke with theCUBE's Rebecca Knight at theCUBE's Coverage of Google Cloud at HIMSS25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the need for responsible implementation of AI in healthcare. (* Disclosure below.) AI in healthcare demands explainability, transparency and trust Healthcare AI solutions rely heavily on data, making privacy and security paramount. AI models must be trained on accurate and diverse datasets because any shortcomings in data integrity can lead to erroneous outcomes, ultimately compromising patient safety. Furthermore, with regulations continuously evolving, organizations must be proactive rather than reactive in adapting their AI strategies to meet compliance standards, according to Rajagopal. "Starting with data, it's important for any of these solutions to have the right datasets to support model training and things like that," he said. "If you don't get your data right, you get into a lot of complexity when you start testing it and rolling it out. The second most important thing is regulation. The regulation continues to evolve, and you must prepare yourself for today and tomorrow." Healthcare operates in one of the most strictly regulated environments. Balancing the need for AI-driven efficiency with stringent privacy laws such as the Health Insurance Privacy and Accountability Act is no easy feat. AI models that impact patient care must adhere to legal requirements to prevent unintended consequences, according to Gupta. "I'm the industry co-chair of the Coalition of Health AI, and we have built a constant color model card," she said. "It's like when you have a nutrition label in a yogurt, you see the ingredients on it. You need to be much more open and transparent about [data usage], and thousands of health systems across the globe are adopting that as a standard." Organizations must anticipate future regulations by continuously evaluating their AI models for compliance. Companies should integrate change management strategies that ensure AI-driven decisions -- such as prior authorizations -- align with legal and ethical standards, according to Rajagopal. "Don't wait for somebody to tell you what to do because one of the key aspects is how you make this work is through effective change management and communication and things like that," he said. "You don't want to be rejecting a prior authorization for some patient and then it bounces back into a problem by itself." Despite AI's ability to process vast amounts of data, human involvement remains critical, especially in healthcare, where empathy plays a fundamental role. AI should support, not replace, human decision-making. Training AI models to incorporate empathetic elements is essential, but the final touch of human compassion must always be present, according to Rajagopal. "When you're building these tool sets to support those decision-making processes, you need to be able to get a little bit of an empathetic view alongside the scientific one, as well, especially [in the case of] a pediatric patient," he said. Here's the complete video interview, part of SiliconANGLE's and theCUBE's Coverage of Google Cloud at HIMSS25:

The role of agentic AI in healthcare - making a difference in workflows and decision-making

Agentic AI is being discussed across industries, and one sector that has the potential to benefit is healthcare. Unlike traditional rule-based automation, agentic AI systems possess the ability to plan, adapt, and interact dynamically with other systems or agents. These capabilities have the potential to be ideal for addressing complex, high-stakes workflows - but how does this work in healthcare, where variability and individualized decision-making are literally a matter of life or death? To get some answers I spoke with Rajan Kohli, CEO of CitiusTech, about the role of agentic AI in healthcare, its technical implications, and the challenges that need to be navigated. Kohli explains that agentic AI is designed for environments where those rules are not fixed. It can learn from patterns, make sure tasks are completed, and manage complex workflows where traditional automation falls short. He explained: Your agent should be able to plan work. It should have memory and persistence. It should know what needs to be done and go back to complete tasks when necessary. Sometimes, multiple agents will collaborate to complete a task. One example Kohli provides is the ICU discharge process. In ICU, it's very expensive to keep a patient there longer than necessary, but it's also risky to discharge them too soon. A static checklist won't work because each patient's case history, diagnosis, and treatment pathway are different. This is a great place for agentic AI because it can generate a tailored checklist based on a large language model, considering patient-specific data. The doctor remains in the loop for final decisions, but the AI significantly reduces the time and cognitive load required. Beyond ICU discharge, Kohli outlines several use cases where agentic AI is already making a difference: Given the sensitive nature of healthcare data, governance is a critical factor in the deployment of agentic AI. Kohli emphasizes the need for robust safeguards, including regulatory compliance and controlled access mechanisms. You typically have another agent -- what we call a governing agent -- that defines the parameters and rules under which the agentic AI operates. Since it's a learning system, a static governance structure won't work; you need a dynamic system that can evolve. Data privacy is another key concern. Healthcare enterprises must ensure that AI models do not process personally identifiable information (PII) in ways that violate HIPAA or GDPR regulations. Kohli notes: The first rule enterprises should follow is feeding anonymized data into the AI system. Personal identifiers should be stripped out before processing. However, he argues that transparency with patients is also valuable: If I'm interacting with an AI system, I think it's okay for the system to disclose that. In fact, it's beneficial -- patients may be more understanding if they know an AI is assisting them, rather than expecting flawless human-like service. The scalability of agentic AI is a common topic. Kohli believes that healthcare is uniquely positioned to scale AI adoption due to its data-rich environment and ongoing demand for efficiency improvements. In the past, healthcare faced major challenges integrating siloed data from multiple sources. Generative AI helps overcome this, as it can process and make sense of diverse data formats. Unlike traditional AI, agentic AI doesn't require as many data scientists to function -- it relies more on data engineers, who are more readily available. He also highlights an impending shift in workforce composition: In application development, maintenance, and system design, we'll need fewer junior developers and more senior engineers. The industry's talent pyramid will shift, with a higher proportion of architects and experienced developers. Beyond technology, Kohli emphasizes the role of process change, emphasizing that scaling AI is not just about the model or technology stack -- it's about rethinking processes, observing: "In fact, I'd say 70% of successful AI scaling is about process change." Many healthcare organizations have already tested agentic AI in controlled environments. The next challenge is moving from pilot programs to full-scale deployment. According to Kohli, nearly all organizations are now asking the same question: how do we scale this? Most healthcare enterprises are dealing with multiple AI systems -- some from major SaaS vendors, others built in-house. They need a control tower to manage these diverse AI systems and ensure interoperability. That's a common discussion we're having across the board. There will be key challenges around governance, scalability, and adaptability to change for organizations looking to move from experimentation to large-scale implementation. As AI becomes embedded into individual systems, the ones who don't play nicely with others will manifest themselves quickly - and any lack of governance from AI providers claiming that agents can do all the work could lead to a life or death situation. Is healthcare the right vertical to lead that transformation by example?

AI-powered healthcare innovations for better patient care

Since artificial intelligence analyzes patient data to create customized treatment plans based on genetics, lifestyle and medical history, AI-powered healthcare innovations are deemed a stepping stone toward better outcomes. Onix Networking Corp. has launched a tool called Expanse that summarizes electronic health record data for healthcare providers, according to Ronald Rerko (pictured, right), practice director of healthcare and life sciences at Onix. "What this search and summarization tool does is it can go through the [electronic health records], crawl through it in other records, bring it together and summarize it into an executive summary to put it as simply as possible for the physician to look at and say, 'What are the most important points I need to know about this patient?,"' Rerko said. "How do we take all that information, digest it down? It's clinical decision support. This is merely trying to pull the information together so you can make the best diagnosis that you can." Rerko and Shweta Maniar, global director of healthcare and sciences at Google Cloud, spoke with theCUBE's Rebecca Knight at theCUBE's Coverage of Google Cloud at HIMSS25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed why AI is becoming central to healthcare innovation (* Disclosure below.) Why partnerships are at the heart of AI-powered healthcare innovations With a collaboration spanning more than 20 years, Onix is a longstanding Google Cloud partner. This partnership leverages Onix's extensive industry experience alongside Google's transformative technologies to assist organizations in various areas, such as AI-powered healthcare innovations, according to Rerko. "Onix is a thirty-year-old company," he said. "We were the first partner with Google back in 2003. We're working very closely with our partners, Meditech and Google, in looking at the other hospital systems that Meditech has their EHR already implemented in and trying to roll it out into those institutions, too. We're working with partners and saying, 'How do you best implement this into a system?'" Onix is accelerating the adoption of AI-powered healthcare innovations to help reduce administrative burdens on healthcare providers, according to Rerko. By streamlining tasks such as scheduling, billing and medical record management, AI allows doctors to spend more time focusing on patient care. "They're spending a third of their day on administrative tasks," he said. "That's limiting the amount of time that they can actually spend with the patient. "One of the doctors I recently spoke with said, 'If you can just give me 40 seconds per patient a day, I will love you forever.' They come to me saying, 'Give me technology solutions that enable me to see the patient, talk with the patient and really provide the best data back to them and the best information to the patient for their care."' Security and privacy in patient data handling are crucial to maintaining trust, ensuring compliance with regulations and protecting sensitive medical information from unauthorized access or breaches. This explains the importance of data and AI governance in healthcare, according to Maniar. "Everything that we're doing from a Google Cloud perspective and with our partners, holds the utmost, the highest level of scrutiny when it comes to our security and privacy, supporting organizations with their [Health Insurance Portability and Accountability Act] compliance," she said. "Partners, again, like Onyx, continue to enhance that as well. Part of the way that we're seeing these solutions and tools have a role in the workplace for hospital systems and clinics is around being able to provide these types of efficiencies so that we can let doctors be doctors and caregivers be caregivers." Here's the complete video interview, part of SiliconANGLE's and theCUBE's Coverage of Google Cloud at HIMSS25:

AI-powered healthcare innovations for better patient care - SiliconANGLE

Since artificial intelligence analyzes patient data to create customized treatment plans based on genetics, lifestyle and medical history, AI-powered healthcare innovations are deemed a stepping stone toward better outcomes. Onix Networking Corp. has launched a tool called Expanse that summarizes electronic health record data for healthcare providers, according to Ronald Rerko (pictured, right), practice director of healthcare and life sciences at Onix. "What this search and summarization tool does is it can go through the [electronic health records], crawl through it in other records, bring it together and summarize it into an executive summary to put it as simply as possible for the physician to look at and say, 'What are the most important points I need to know about this patient?,"' Rerko said. "How do we take all that information, digest it down? It's clinical decision support. This is merely trying to pull the information together so you can make the best diagnosis that you can." Rerko and Shweta Maniar, global director of healthcare and sciences at Google Cloud, spoke with theCUBE's Rebecca Knight at theCUBE's Coverage of Google Cloud at HIMSS25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed why AI is becoming central to healthcare innovation (* Disclosure below.) With a collaboration spanning more than 20 years, Onix is a longstanding Google Cloud partner. This partnership leverages Onix's extensive industry experience alongside Google's transformative technologies to assist organizations in various areas, such as AI-powered healthcare innovations, according to Rerko. "Onix is a thirty-year-old company," he said. "We were the first partner with Google back in 2003. We're working very closely with our partners, Meditech and Google, in looking at the other hospital systems that Meditech has their EHR already implemented in and trying to roll it out into those institutions, too. We're working with partners and saying, 'How do you best implement this into a system?'" Onix is accelerating the adoption of AI-powered healthcare innovations to help reduce administrative burdens on healthcare providers, according to Rerko. By streamlining tasks such as scheduling, billing and medical record management, AI allows doctors to spend more time focusing on patient care. "They're spending a third of their day on administrative tasks," he said. "That's limiting the amount of time that they can actually spend with the patient. "One of the doctors I recently spoke with said, 'If you can just give me 40 seconds per patient a day, I will love you forever.' They come to me saying, 'Give me technology solutions that enable me to see the patient, talk with the patient and really provide the best data back to them and the best information to the patient for their care."' Security and privacy in patient data handling are crucial to maintaining trust, ensuring compliance with regulations and protecting sensitive medical information from unauthorized access or breaches. This explains the importance of data and AI governance in healthcare, according to Maniar. "Everything that we're doing from a Google Cloud perspective and with our partners, holds the utmost, the highest level of scrutiny when it comes to our security and privacy, supporting organizations with their [Health Insurance Portability and Accountability Act] compliance," she said. "Partners, again, like Onyx, continue to enhance that as well. Part of the way that we're seeing these solutions and tools have a role in the workplace for hospital systems and clinics is around being able to provide these types of efficiencies so that we can let doctors be doctors and caregivers be caregivers." Here's the complete video interview, part of SiliconANGLE's and theCUBE's Coverage of Google Cloud at HIMSS25: