We've all consulted with a medical expert for a diagnosis of what ails us. After the examination, the doctor pulls out a chart on a clipboard, taps into a brain loaded with medical education and institutional knowledge, furrows a brow, then delivers the news in an informed and sympathetic bedside manner.
But as artificial intelligence pushes the boundaries of healthcare, this scene plays out a little different today.
At the touch of a tablet screen, medical experts now have the benefit of an instant diagnosis and treatment plan from information derived through AI, machine learning, and data analytics.
According to the World Health Organization, AI can be used to improve the speed and accuracy of diagnosis and screening for diseases. AI-based solutions can assist with clinical care, strengthen health research and drug development, and support diverse public health interventions.
The healthcare industry is constantly finding new ways to develop devices with AI and ML capabilities. Microprocessors, microcontrollers, and field-programmable gate arrays ferry data and algorithms to construct informed diagnoses. These devices can create better health-record management databases for risk prediction. The potential for healthcare-driven artificial intelligence—from AI-powered products and applications to robots—can benefit doctors and patients alike.
This is good news for design engineers who are seeking to build new solutions where innovative products abound.
In this week's New Tech Tuesdays, we'll look at products from NXP Semiconductors, Microchip Technology, and Advantech that help in the development of AI, ML, and data analytics.
NXP Semiconductors' comprehensive eIQ™ Machine Learning (ML) Software Development Environment makes ML at the edge possible for all levels of developers who must securely turn observation in automation. The environment integrates ML workflow tools, neural network compilers, inference engines, and libraries to support a range of compute engines. This gives engineers and application designers the flexibility and freedom they need to develop products with advanced ML technologies. This software also offers the key ingredients to deploy various ML algorithms at the edge. The eIQ environment takes advantage of existing hardware to accelerate ML application development without requiring hardware specific for ML.
Microchip Technology Machine Learning is a category that includes software and hardware tool kits, reference designs, and silicon platforms for designers who are experienced or new to ML or AI. Microchip's AI- and ML-based algorithms can collect and organize data, train neural networks in data centers, or implement optimized inference on the edge. Microchip's portfolio of silicon devices includes microcontrollers, microprocessors, and FPGAs. Microchip's software toolkits feature ML frameworks such as TensorFlow, Keras, Caffe, and others. The hardware and software combination enables designers to develop application solutions that include AI acceleration cards for data centers, self-driving cars, security and surveillance, electronic fences, augmented and virtual reality headsets, drones, robots, satellite imagery, and communication centers.
The Advantech MIC-710A1 AI Inference System is a compact, fanless computer that's ideal for use in industrial AI applications. The computer is based on the NVIDIA® Jetson Nano™ Module and features a quad-core Arm® Cortex®-A57 processor, a Maxwell GPU with 128 NVIDIA CUDA® cores, 4GB LPDDR4, and 16GB Flash. The MIC-710A1 has dual Gigabit Ethernet LAN ports, HDMI video for display, an internal USB 2.0 and external USB 2.0 and USB 3.0 ports, and an 8-bit DI/DO port for signal control.
Artificial intelligence is pushing the boundaries of healthcare. Like all technologies, issues of ethical standards and security are concerns. But the advantages of accurate data-driven diagnoses outweigh the disadvantages. All that's needed is a programmable bedside manner. Chances are someone is already working on it.
Tommy Cummings is a freelance writer/editor based in Texas. He's had a journalism career that has spanned more than 40 years. He contributes to Texas Monthly and Oklahoma Today magazines. He's also worked at The Dallas Morning News, Fort Worth Star-Telegram, San Francisco Chronicle, and others. Tommy covered the dot-com boom in Silicon Valley and has been a digital content and audience engagement editor at news outlets. Tommy worked at Mouser Electronics from 2018 to 2021 as a technical content and product content specialist.