108 FEATURE technology stack. Microsoft provides the technology infrastructure and tools that decrease the risk and impact of cyberattacks. Building on top of that, we provide highperformance storage and compute capabilities to help organisations manage large data sets and multimodal AI models. Further up the technology stack, we enable efficient data management by leveraging interoperability standards such as HL7 FHIR and offering secure environments that promote privacypreserving data collaboration.” In October 2024, Microsoft updated Microsoft Cloud for Healthcare with new AI tools, including healthcare application templates for Microsoft Purview and solutions to make data more accessible in Microsoft Fabric. There are also new healthcare multimodal healthcare models in Azure AI Studio and GitHub, and organisations can use healthcare agent services in Microsoft Copilot Studio to build their own secure copilots with healthcare-specific features. They can be used for applications such as scheduling appointments and allocating treatments. “AI is dependent on data sets – and the more sources of data that are available, the more robust the AI models become,” says Rhew. “Microsoft Fabric enables organisations to ingest, transform and manage multimodal data sets, while Microsoft Purview enables streamlined governance of the data. “Microsoft facilitates the deployment of AI models by connecting multimodal data sets to AI development environments such as Azure AI Studio and GitHub. Healthcare agent services integrated in Microsoft Copilot Studio can help enable the creation of agentic AI.” A helping hand One key benefit of AI technology for healthcare is its ability to automate routine tasks, which boosts operational efficiency and productivity, while reducing the administrative burden on workers and freeing them up to focus on patients. For example, AI can be used to risk-stratify patients. “A major challenge in healthcare is the shortage of clinicians, which contributes to increasing wait times and both patient and clinician dissatisfaction,” says Rhew. “The wait time problem is further compounded by inefficient triage methods. In a busy emergency department AI applied to radiology images can help identify high-risk individuals requiring urgent attention. The technology can also identify low-risk patients who could potentially be managed through an alternative pathway. AI-enabled risk-stratification followed by triage and capacity building help make care more efficient, and in the process, improve outcomes.” In addition, AI can be used to rapidly analyse clinical data and help clinicians and nurses write reports and other documents. “Public healthcare can be highly fragmented, administrative, and dependent on large data sets,” says Rhew. “The process of acquiring, curating and analysing data, and reporting results is time consuming and inefficient, but generative AI can help in many ways. First, administrative tasks such as data collection, report writing and presentation of results can be performed by generative AI. The technology can also write programming code that can help automate tasks.” Generative AI can also power virtual assistants with natural language capabilities to allow both patients and healthcare professionals to quickly access the information or service they need. “Chatbots powered by generative AI may be used to help individuals answer questions and sort through complex problems,” says Rhew. “AI-based chatbots can communicate with individuals in their own language and at their grade level, which enables this technology to be used by anyone, anywhere.” Microsoft’s Dragon Ambient eXperience (DAX) Copilot, for example, is helping hundreds of healthcare organisations to streamline administrative tasks and document patient visits directly in electronic health records. DAX Copilot combines Dragon Medical’s natural language voice dictation capabilities – used by more than 600,000 clinicians worldwide – with ambient and generative AI to automatically convert multiparty conversations into specialityspecific standardised draft clinical summaries that integrate with existing workflows. In 2024, 77 per cent of 879 clinicians surveyed by Microsoft said using DAX Copilot “ AI is dependent on data sets – and the more sources of data that are available, the more robust the AI models become”
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