Invest in Information Technology Infrastructure for the Implementation of AI in the Healthcare Sector
In the rapidly evolving landscape of healthcare, artificial intelligence (AI) is increasingly becoming an integral part of various operations. The challenge lies in standardizing platforms for hardware providers and independent software providers to meet the needs of healthcare IT administrators and system integrators [1].
As AI-powered applications become more deeply embedded across healthcare organizations, they bring forth new challenges that necessitate more robust solutions, compact form factors, and energy-efficient hardware [2]. These AI models must reside within the healthcare organization's IT infrastructure [3].
The growth in healthcare, medical systems, life sciences, and scientific equipment over the past eight years has fueled the demand for AI applications in the sector [4]. Automatic prioritization protocols are essential in the software layers for AI applications, particularly for low latency, which is crucial for inferencing results [5]. High performance computing, robust cloud infrastructure, and a lot of memory load are required for the AI training side.
The AI models used in healthcare must be optimized for edge computing in appliances and medical systems [6]. A more heterogeneous architecture offers a better opportunity to accomplish tasks with high throughput, low latency, and high accuracy [7].
Specific AI applications currently used in healthcare include AI-powered imaging diagnostics, AI-driven decision support systems, AI in cardiology, robotic surgery and virtual health assistants, administrative automation, and automated patient communication [8].
AMD technologies play a significant role in the adoption and effectiveness of these AI applications in healthcare. AMD Ryzen Mobile Processors provide powerful, energy-efficient computing capabilities for mobile and edge healthcare devices, facilitating AI inference locally [9]. AMD EPYC Processors deliver high-performance, scalable CPU computing to handle complex AI workloads such as data integration, decision support, and predictive analytics in hospitals and data centers [10].
AMD Instinct Series Accelerators are GPUs specialized for AI training and inference, offering up to 4x generational improvements and massive compute density to accelerate AI model development and deployment, including imaging diagnostics and robotic surgery [11]. The Versal AI Core Series are adaptive compute platforms that combine scalar, vector, and AI engines, suited for real-time AI inference and edge healthcare applications requiring low latency and high reliability [12].
Vitis AI Software is an open software platform enabling AI model deployment across AMD hardware, optimizing AI workflows for healthcare applications, and enabling smoother integration of AI into clinical environments [13]. Together, these AMD technologies create a robust AI infrastructure that supports demanding healthcare AI workloads—from training large models to performing real-time AI inference—enabling faster, more accurate diagnostics, personalized care, operational efficiency, and ultimately better patient outcomes [14].
Healthcare providers must carefully evaluate their existing infrastructure for potential bottlenecks, considering network bandwidth, storage capacities, data security, and seamless integration with existing systems. AMD is dedicated to developing AI solutions in healthcare, and a strategic approach to healthcare IT infrastructure planning is needed to harness the full potential of AMD solutions [15]. Healthcare IT professionals must prioritize scalability and flexibility to accommodate future advancements in AI technology.
Artificial intelligence (AI) models, residing within healthcare organizations' IT infrastructure, require energy-efficient hardware capable of edge computing, as they are increasingly deployed in various applications across the sector. For instance, AMD Ryzen Mobile Processors provide energy-efficient computing capabilities for edge healthcare devices, enabling local AI inference for AI-powered imaging diagnostics or virtual health assistants.
A more heterogeneous architecture, offering high throughput, low latency, and high accuracy, is beneficial in meeting the diverse needs of AI applications in healthcare, such as cardiology, robotic surgery, and administrative automation. AMD Instinct Series Accelerators and Versal AI Core Series, with their AI training and inference capabilities, help accelerate AI model development and deployment for these applications.
