A Top Priority

Technological developments in recent decades have been a driving force in improvements in medicine. Today’s medical devices give doctors and others in the field far greater ability to see details that would previously have been missed, and the dropping cost of medical technology makes these devices more accessible. However, these advantages come with some downsides. In particular, poor security can put patient data at risk, and certain types of malware can render devices inaccurate or even unusable. Cybersecurity now must be a top priority for practices of all sizes.

Will it Impact How Medicine is Practiced?

The medical field is notoriously reluctant to adopt new technologies, as the high stakes of changing how medicine is practiced means new technologies have to be thoroughly vetted before being implemented. However, the promise of blockchain technologies means a growing number of medical entities are taking a close look at using it as a foundational technology across a range of areas, and this momentum is only expected to accelerate in the coming years. Here are some of the reasons why blockchain technology is poised to have a major impact on how medicine is practiced worldwide.

Revolutionizing IoT

The term “LTE” is certainly familiar to anyone following mobile connectivity over the years. For those operating in the embedded space, however, a technology called LTE-M is revolutionizing how Internet of Things and other connected devices can operate across a wide range of industries. Here are a few facts about this burgeoning technology and its present and future impact on the embedded environment.

Accelerating AI

Despite how complex modern computers seem, their basic architecture hasn’t changed much since they started implementing what’s known as Von Neumann architecture. The real “brains” of computers lie in the CPU and the RAM, the latter of which stores program code and data. While modern RAM is amply fast for a wide range of tasks, the connection between the RAM and CPU can function as a bottleneck for certain tasks; in particular, many artificial intelligence implementations are restrained by how quickly the processor can access data stored in RAM. Recent developments, however, aim to mitigate this limitation, potentially leading to far more capable AI implementations and allowing developers to craft new means of harnessing the potential of AI.

How They Interact

Artificial intelligence, and machine learning in particular, is transforming various fields in a profound manner. Although the medical field has a reputation for being slow to adopt various technologies, AI and ML are currently changing how medical devices are being designed, potentially leading to better outcomes for patients. The FDA is taking an active role, helping to provide guidance for medical device designers and manufacturers. However, the fundamental nature of AI and ML presents challenges for regulation, and the FDA is in the process of developing regulations that both allow potentially useful devices to come to market while ensuring patients and doctors can expect devices to be safe and beneficial.

Is It Finally Coming?

Medical devices must meet strict scrutiny. In order to be sold in nearly any market, a device needs to be rigorously shown to be effective and safe. However, medical device designers and manufacturers have long had to deal with competing regulations, most notably the FDA Quality System Regulation, which sets the rules for devices sold in the United States, and the ISO 13485, an international standard used in the European Union, Japan, Canada, and other nations. In order to ease the process of bringing products to the market, the FDA has a goal of harmonizing its requirements with those of ISO 13485, but various factors have delayed this transition and even raised questions over how and whether it can be implemented in a timely manner.

US Task Force Plans to Share Government Data with Academia and Private Sector

On June 10th, 2021, the White House administration launched an effort to provide additional government data to artificial intelligence researchers. The move, which aims to ensure the United States remains at the forefront of AI research, will provide invaluable data to academia and the private sector for developing new tools.

In addition to offering access to data, the initiative may also provide access to government-run computing power, which can be especially useful for cutting-edge academic research.

The initiative seeks to provide a broad range of data, including information about health, driving habits, and demographics.

The Matter Standard

Although Google, Apple, and Amazon are often fierce competitors, there are areas where collaboration between these tech giants can prove beneficial for all. When it comes to the use of Internet of Things devices in residential and commercial environments, adoption has been slow. An initiative formerly known as Project CHIP was recently renamed Matter, and its wide support is poised to provide benefits to tech giants and startups alike.

Helping Medical Device Innovation

While advances in the scientific process, imaging, and other factors have played a major role in the advancement of medicine, medical devices have been one of the primary linchpins driving advances that extend and improve peoples’ lives. Ensuring these devices are safe and effective, however, is a critical task: Poor regulation can lead to a market marred by ineffective and, even worse, dangerous products. In the United States, the FDA is at the forefront of this task.

Two Linked Technologies

The concept of cloud computing has matured over the years, and its benefits are clear. Serving as the connection between end devices and the cloud, the edge’s role has transitioned from a necessary compromise to the cloud paradigm to a valuable tool to improve operations. With the rollout of 5G technology, edge computing is set to undergo a bit of a revolution. Here are some of the ways these technologies will interact in the coming years.

Innovative Uses

Anyone who’s used a smartphone has experience with capacitive touch devices. While capacitive technology is mature and affordable, there is still ample untapped potential, and innovative user interfaces that are coming to the market at a rapid pace.

Capacitive touch devices are generally considered to be the first touch screens, invented in the mid-1960s. However, it was resistive touch screens that became popular, particularly when touch technology took off in the 1990s.

Resistive screens and interfaces physically measure when pressure is applied to the surface, giving them excellent accuracy but requiring a significant amount of force to use.

The New Date Is May 26, 2021

The European Medical Device Regulation (MDR) is finally set to take over, marking a significant shift from the Medical Device Directive (MDD). While medical device manufacturers and many entities involved in the supply chain received a bit of a reprieve when the date moved back from its initial scheduled launch in May of 2020, regulators have indicated that there will be no further delay despite challenges posed by the scope of the new directive and the COVID-19 pandemic.

High performance, Low Power, Low Cost BLE for IOT Applications

One of the challenging aspects of placing increasing numbers of IOT sensors into consumer and industrial applications is compromised link budget due to non-ideal placement of devices around absorptive or conducting surfaces, and non-ideal antenna and packaging configurations, as well as long range.

Benefits Include Increased Performance, Reduced Power Needs and Enhanced Miniaturization

The global 3D IC (three-dimensional integrated circuit) market will reach a value of $22.30 Billion by 2027. The increasing demands for smart devices and the importance of miniaturization in the electronics industry are influencing the market growth.

As it's become more difficult to shrink existing processes, stacking up is the way to increase integration. Stacking can be done die-on-die, die-on-wafer, wafer-on-wafer, or in combination.

What is Production Equivalent?

In the FDA’s 21 CFR Part 820: 820.30(g) Design validation requires that devices used for validation are "production units, lots, or batches, or their equivalents.”

Although 21 CFR 820.30 does not require design verification on production equivalent devices, in general, it is beneficial to perform final system-level verification, standards compliance testing, and verification of risk controls using production equivalent devices because this provides the confidence that any changes made throughout the development process did not impact the ability of the device to meet requirements.

Power Consumption Design Challenges

The COVID-19 pandemic has created an unprecedented need for remote patient monitoring. Most of the patients who test positive for COVID-19 are in self-isolation at home, increasing the need for alternative medical solutions, including remote patient monitoring. On March 20, 2020, The FDA issued guidance for expanded use of certain remote monitoring devices to facilitate patient management while limiting physician-patient contact during the COVID-19 pandemic.

From a Static Regulation Model Towards Regulation as Iterative Checkpoints

Artificial Intelligence (AI) has the power to improve health outcomes for patients because AI can distinguish patterns in data that are not discernible to the clinician. These patterns rely on a supply of health data to train machines that learn responses to diagnose, predict, or perform more complex medical tasks.

Traditional, or non-adaptive, Machine Learning (ML) utilizes two separate paths--training and prediction, whereas adaptive AI uses a single path process that monitors and learns the new changes made to the input and output values and their associated characteristics.

Analyzing Data Locally Within Devices

With the increasing performance and power efficiency of embedded processors, there comes the ability to embed sophisticated machine algorithms in deeply embedded systems, bringing computing and machine learning closer to where sensors gather data.

Smart Sensors are Modernizing Connectivity and Analytics

The internet of things (IoT) is getting smarter due to increased processing capabilities of embedded processors, distributed processing and data reduction capabilities of embedded systems and gateways. Smart buildings, smart cities, and now even wearable devices can use smarter sensor processing to compile and process data locally, allowing more efficient utilization of valuable communication bandwidth, as well as allowing intelligent decision making locally versus having to go to the cloud and back.

The Benefits of an Algorithmic Approach to Medicine

The purpose of a medical algorithm is to improve the delivery of medical care by removing some of the uncertainty from medical decision-making.  These algorithms are able to predict and diagnose diseases with extreme accuracy.  They are also able to predict related factors such as the likelihood of death, the length of hospital stays, and the chance of hospital readmission