Balancing AI Potential in Medical Devices: Optimization and Human Thinking

The integration of artificial intelligence (AI) into medical devices has shown tremendous promise in revolutionizing patient care, diagnosis, and treatment. From detecting subtle anomalies in medical images to offering predictive insights, AI-powered medical devices offer remarkable potential.

However, as society embraces this transformative technology, it’s crucial to strike a balance between harnessing AI’s capabilities and preserving the indispensable role of human expertise. One key consideration is the optimization of both hardware and software in AI-driven medical devices. Cutting-edge AI algorithms are only as effective as the hardware they run on.

Ensuring that devices are equipped with the necessary processing power and memory to handle complex AI computations is paramount. Moreover, ongoing software updates are essential to refine AI algorithms, enhance accuracy, and address emerging medical challenges. Regular optimization ensures that medical devices remain relevant and effective throughout their lifespan.

While AI can greatly enhance efficiency and accuracy, it must not overshadow the importance of human thinking. Medical professionals possess a wealth of knowledge, experience, and intuition that AI cannot replicate. The human touch in diagnosis and treatment encompasses empathy, ethical judgment, and critical thinking, which are vital in complex medical cases and patient interactions. AI should be seen as a valuable tool that aids medical professionals rather than replacing their expertise.

To strike the right balance, interdisciplinary collaboration is crucial. Engineers, data scientists, and medical practitioners must collaborate to develop AI-powered medical devices that align with clinical needs and ethical standards. Regular feedback from healthcare providers can help fine-tune algorithms and ensure that AI recommendations are clinically sound as well as patient-centric.

Medical Device Tech Trends to Watch: AI / ML, Cybersecurity, and Medical Robots

robot and medical digital image

After a pause in progress during the pandemic, medical device companies are once again making strides to innovate in many areas of emerging technologies as a way to manage data and perform services more effectively and securely. Some of the most exciting trends to watch in medical device technology right now involve artificial intelligence, cybersecurity, and medical robots.

Artificial Intelligence (AI) and Machine Learning (ML): New AI and ML tools are increasingly being used to collect, curate, and analyze patient information to increase the speed and accuracy of data analysis while freeing up employees for other crucial tasks. “The new AI and ML features provide workers with new and important insights deriving from the growing amount of data from medical records,” reports Med Device Online, resulting in improved diagnostic decision-making with high levels of precision and more efficient doctor interventions.

Cybersecurity: With emerging technologies comes new and greater security risks, including more sophisticated attacks by hackers, so cybersecurity for medical devices has never been more important. The number of cybersecurity attacks targeting U.S. healthcare organizations “doubled in the first half of 2022 compared to 2021,” according to Med Device Online, which said this increase is mainly due to lack of cybersecurity expertise among employees.

Those purchasing and managing medical devices must be concerned with two aspects of device security, according to product design company Plexus. First, they must understand how devices are designed to control and protect customer information. It’s essential for companies to protect sensitive patient medical information as it is shared within an organization or with other trusted stakeholders. The wider medical networks are, the greater the risk of a cyberattack that can quickly spread from one device to another.

Companies also must have a plan to provide security for medical devices against external threats once put into use. Aside from implementing proven cybersecurity measures such as authentication requirements, limiting the duration of user sessions, and checking for data compliance, among others, some organizations are also adopting blockchain as a solution to enable safe interactions and facilitate secure cryptocurrency transactions.

Medical Robots: When the pandemic prompted shortages of qualified staff due to burnout or other causes, medical device companies invented new robots to help perform tasks, and those robots are here to stay, says Med Device Online. Robots can perform routine medical tasks such as performing venipuncture, monitoring patient vitals, and disinfecting rooms.

Surgical robots in particular are by far the leading category of robotics currently in use, and demand for this technology has seen immense growth in recent years, according to tech trend analysis site Exploding Topics. It is widely reported that surgical robots such as the da Vinci and Hugo systems are becoming more capable and continue to increase the types and level of sophistication of the tasks they perform. This industry is expected to exceed $20 billion by 2028, according to Verified Market Research.

Accelerated Speed to Market

The COVID-19 pandemic forced many medical device companies to learn new ways of accelerating design, development, and testing of products to respond to pandemic threats more effectively, and they now benefit from these lessons when developing new diagnostic instruments. “The life science companies that are prepared to offer that kind of rapidly developed, rapid-response technology at the point of use are the ones who will be better positioned to handle the next global health crisis,” notes Plexus.

Ultimately, though the lessons learned during the pandemic were difficult, they resulted in many useful innovations that will continue to benefit the medical device sector for years to come.

Boosting Supply Chain Resiliency with Data Analytics and Artificial Intelligence

Boosting Supply Chain Resiliency

Coming out of the COVID-19 pandemic, many organizations are considering digital transformation as the key to ensuring supply chain resilience. Evolutions in technologies have enabled the creation of digital supply networks that can be used by companies to strengthen their procurement strategy. With new and emerging digital solutions involving artificial intelligence (AI) and analytics, companies now can harness their data to optimize inventory more effectively than ever before.

Even as the pandemic is receding, supply chain disruptions continue to be commonplace, and chief supply chain officers are under growing pressure to use and analyze real-time data to mitigate risk. According to a recent Forbes article, “43% of enterprises will continue to digitalize and integrate innovative technology into enterprise-wide systems. This means that in the coming year, the ability to augment operations and decision-making with data analytics will continue to be a transformative and highly favored capability.”

Forbes reports that implementing new data analytics capabilities is best considered as a series of digital initiatives, for which there are many options. For instance, the introduction of the metaverse is one solution that will greatly increase a company’s ability to deliver predictive insights across supply chain networks, enabling it to “reduce development times and risk, achieve higher operational efficiency, and improve resilience.”

Ensuring Continuity Throughout Disruptions

Companies are using analytics and AI to mitigate risk and ensure continuity throughout any global supply chain disruptions. These powerful tools help businesses automate tasks in a way they never could before, while gaining deeper insights for better, faster decision-making, according to Supply Chain magazine.

Supply Chain reports that digital twin technology is currently considered one of the most innovative uses of AI and data analytics in supply chains. A digital twin provides a virtual supply chain replica that enables scenario modeling to simulate the impact of disruptions, like market changes and natural disasters, allowing companies to determine how resilient their supply chain is. AI modeling also proactively identifies supply risk, such as a supplier’s inability to source needed materials, before it can become a problem.

Deloitte experts agree that “with improvements in data, analytics, computing power, and visualization, digital procurement has better evidence-based options for decision-making, which can improve both the value and accuracy of strategic decisions and the speed of execution.”

New disruptive digital supply chain technologies are altering the procurement function for the better. Deloitte recommends that procurement leaders invest in both:

  • Maturing digital solutions that are currently transforming procurement with minimal investment, such as cognitive computing/artificial intelligence, predictive/advanced analytics, intelligent content extraction, visualization, and crowdsourcing
  • Emerging digital solutions that could impact procurement in the future, such as block chain, sensors/wearables, cyber tracking, and virtual reality/spatial analytics

In the face of continuing disruption, companies must adapt to ensure supply chain resilience. Digital transformation is an incremental, multi-year journey. If your company has not yet created a digital procurement strategy and started this process, now is the time to consider the many AI and data analytics options available on the path to intelligent supply chain management.

How Artificial Intelligence Is Driving Product Lifecycle Management

How Artificial Intelligence Is Driving Product Lifecycle Management

Today artificial intelligence (AI) is increasingly being integrated into platforms across industries, and the healthcare sector is no exception, with AI being used to support medical device Product Lifecycle Management (PLM).

The ability to apply machine learning to PLM systems can help medical device manufacturers drive insights more effectively from product data that has been collected over many months, or even years, said digital transformation provider Kalypso. This sort of “product lifecycle intelligence” applies AI and automation to help users develop predictions and to recommend improvements, ultimately allowing manufacturers to prevent costly product delays and failures.

Kalypso detailed how one top medical device manufacturer addressed its global challenges by using AI and machine learning to drive measurable business results. “By combining PLM with product lifecycle intelligence,” they said, “companies can bridge the gap in PLM analytics capability today, allowing them to understand current performance, historical averages, and the variances across different business units and functions. These insights can help them develop more meaningful customer experiences, while driving business and product value.”

A Higher Level of Guidance

AI-powered product insights platforms now exist that help companies create and market their products more effectively, and they can contribute meaningfully to every stage of the product development cycle, said AI innovator Rodrigo Pantigas, CPO and co-founder of AI-driven insights startup Birdie, in a recent article for technology news site VentureBeat. Using the right platform during the different stages of the product development cycle can help companies maximize their return on investment.

For example, when conducting competitive assessments and evaluating trends during the initial development stage, AI-driven product intelligence can provide a higher level of guidance beyond human concept testing and social listening. “Instead of latent indicators caused by surfacing reading of comments today, product intelligence platforms can crunch the totality of conversations to understand where customer preferences are going,” reported Pantigas.

Once a product has been built and is ready to launch, “the right intelligence platforms can tap into existing conversations to understand existing customer perception both about the anticipation of this launch and consumers’ ongoing opinions about the product category and competition—which can also help to identify product issues and crises early on,” said Pantigas. “It allows you to contrast your product against the competitive set, as well as identify channels that could help get your product in front of a much wider audience.”

Real-time Metrics

AI offers the ability to generate real-time metrics on a product’s health, growth, and risk factors, which is “important for successful product managers to add an additional layer of efficiency with strategic and operational decision-making,” noted Skyjed, an AI platform provider for product teams. The ability to access data about a product’s health on the fly helps teams make more effective decisions as market conditions evolve, providing them with a competitive advantage.

Dynamic Technologies has a role to play in helping your company identify and manage the lifecycles of connected hardware and software in your supply chain. Our technology supply chain management services include product sourcing, supply chain design, and management, and product procurement. Review our case studies and learn more about how we can help you with your next project.