Emily Newton, Editor-in-Chief, Revolutionized04.21.23
Artificial intelligence (AI) is making waves across the medical industry. Many of these use cases center around hospital care and pharmaceuticals, but the device production side of healthcare can benefit from AI, too. Manufacturing AI is making medical device production far more efficient and safe.
AI is a broad technology, so it’s possible applications in medical device manufacturing are similarly varied. Here are five ways it can change the industry for the better.
AI is excellent at spotting patterns in data, so it can find inefficiencies or ways to boost productivity better than humans. It can also highlight these potential improvements faster than manual approaches. One study found digital twins led to a 58% reduction in the time it takes to reconfigure a manufacturing workflow.
These improvements could help medical device manufacturers keep up with skyrocketing demand. Improved efficiency would also make adapting to supply chain issues that have rocked the industry easier.
AI also lowers costs related to human error, which accounts for as much as 80% of process deviations in pharmaceutical manufacturing and related fields. Medical device manufacturers can use AI to automate back-office tasks, process analysis, and other data-heavy work to minimize human involvement. This automation, in turn, reduces mistakes, leading to more uptime and less rework, lowering costs.
As medical device companies reduce their operating expenses, they can lower prices for their end customers. This shift will make potentially life-saving devices more accessible, improving healthcare outcomes on a national—if not global—scale.
AI can recognize ideal places or groups to conduct trials to recruit study participants faster. During the tests, AI can automate data collection and organization to prevent human error from compromising results and accelerate record-keeping. Some may also automate regulatory compliance to ensure a smoother approval process.
By making clinical device trials faster, more accurate, and more compliant, AI will accelerate products’ time to market. As a result, manufacturers will reduce their costs, accelerate returns on investment, and help patients get potentially life-changing help in a shorter period.
Quality checks are repetitive and data-intensive, which makes them prone to error when manual but ideal for AI. For example, vibration testing is one of the most effective ways to measure object health but requires careful analysis of waveforms, which involves data that’s easy for humans to miss. By contrast, AI can immediately catch anything falling outside of acceptable parameters, ensuring no product below standards goes to market.
Over time, data from these automated quality checks will reveal trends in production errors. AI models can analyze these trends to highlight where the most errors come from and suggest changes to reduce similar mistakes in the future.
AI technologies are already starting to appear in medical devices. Some wearables use AI to monitor users’ sleep patterns to warn them if they’re at risk of a seizure. Similar devices detect potential cardiovascular issues by analyzing heart rates and other signals with AI.
Just as machine learning can make accurate predictions about manufacturing efficiency, it can analyze biological systems to predict health outcomes accurately. Devices with this functionality could lead to earlier and, consequently, more effective treatment. That advantage will set AI-enabled products apart from less effective, conventional alternatives, so capitalizing on the movement early could lead to impressive growth for manufacturers.
More than half of machine learning users today cite security and compliance as their most significant challenges with the technology. AI requires a considerable amount of data to deliver reliable results, which can make companies a target for cybercrime. The solution is to use synthetic data wherever possible to minimize privacy concerns, store AI in secure, encrypted locations, and restrict access to this data so only employees who need it for their work can view it.
Data cleanliness is another concern. AI is only as accurate as its data, so ensuring this information is error-free and in a standard format is important. Manufacturers can use automated data cleansing solutions to clean and organize their AI data before analysis to improve reliability.
AI is also expensive, so it’s best to start small. Manufacturers should first apply it to the area where they’ll benefit most. As they begin to see a return on their investment, they can expand AI to other workflows, using any lessons learned in the initial implementation.
When medical device manufacturers know how to benefit from AI and what obstacles to watch out for, they can implement it with better results. As they do, they’ll unlock their full potential and ensure they remain competitive in an increasingly tech-centric market.
Emily Newton is the Editor-in-Chief of Revolutionized. She’s always excited to learn how the latest industry trends will improve the world. She has over five years of experience covering stories in the science and tech sectors.
AI is a broad technology, so it’s possible applications in medical device manufacturing are similarly varied. Here are five ways it can change the industry for the better.
1. Streamlining Production Workflows
Digital twins are some of the most popular applications of manufacturing AI. These technologies create virtual replicas of real-life objects, areas, or scenarios, then use AI to run simulations in them. In manufacturing, that often takes the form of testing new workflow changes for the production floor to find ways to make it more efficient.AI is excellent at spotting patterns in data, so it can find inefficiencies or ways to boost productivity better than humans. It can also highlight these potential improvements faster than manual approaches. One study found digital twins led to a 58% reduction in the time it takes to reconfigure a manufacturing workflow.
These improvements could help medical device manufacturers keep up with skyrocketing demand. Improved efficiency would also make adapting to supply chain issues that have rocked the industry easier.
2. Reducing Costs
Implementing AI into manufacturing facilities can also help medical device companies lower operating expenses. Part of these savings comes from AI's increased efficiency, but the technology can reduce costs elsewhere, too.AI also lowers costs related to human error, which accounts for as much as 80% of process deviations in pharmaceutical manufacturing and related fields. Medical device manufacturers can use AI to automate back-office tasks, process analysis, and other data-heavy work to minimize human involvement. This automation, in turn, reduces mistakes, leading to more uptime and less rework, lowering costs.
As medical device companies reduce their operating expenses, they can lower prices for their end customers. This shift will make potentially life-saving devices more accessible, improving healthcare outcomes on a national—if not global—scale.
3. Accelerating Clinical Trials
While manufacturers in all sectors face high efficiency and cost pressures, medical companies face the additional challenge of clinical trials. The premarket trial process for Class III devices can be slow and expensive with conventional approaches, but manufacturing AI can streamline it.AI can recognize ideal places or groups to conduct trials to recruit study participants faster. During the tests, AI can automate data collection and organization to prevent human error from compromising results and accelerate record-keeping. Some may also automate regulatory compliance to ensure a smoother approval process.
By making clinical device trials faster, more accurate, and more compliant, AI will accelerate products’ time to market. As a result, manufacturers will reduce their costs, accelerate returns on investment, and help patients get potentially life-changing help in a shorter period.
4. Improving Quality Control
Quality control is another critical production element manufacturing AI can improve. While this process is crucial for any sector, medical devices must meet exceptionally high standards, making quality control a more mission-critical step. AI offers a faster and more reliable way to perform these tests.Quality checks are repetitive and data-intensive, which makes them prone to error when manual but ideal for AI. For example, vibration testing is one of the most effective ways to measure object health but requires careful analysis of waveforms, which involves data that’s easy for humans to miss. By contrast, AI can immediately catch anything falling outside of acceptable parameters, ensuring no product below standards goes to market.
Over time, data from these automated quality checks will reveal trends in production errors. AI models can analyze these trends to highlight where the most errors come from and suggest changes to reduce similar mistakes in the future.
5. Opening New Product Possibilities
AI also opens up an exciting new field of products for medical device companies to manufacture. Medical devices with embedded AI functionality could revolutionize the healthcare sector. The businesses that make these products will likely see rising demand as their benefits grow increasingly clear.AI technologies are already starting to appear in medical devices. Some wearables use AI to monitor users’ sleep patterns to warn them if they’re at risk of a seizure. Similar devices detect potential cardiovascular issues by analyzing heart rates and other signals with AI.
Just as machine learning can make accurate predictions about manufacturing efficiency, it can analyze biological systems to predict health outcomes accurately. Devices with this functionality could lead to earlier and, consequently, more effective treatment. That advantage will set AI-enabled products apart from less effective, conventional alternatives, so capitalizing on the movement early could lead to impressive growth for manufacturers.
Manufacturing AI Best Practices
AI’s impact on medical device manufacturing is impressive, but it’s essential to recognize that it comes with some challenges. Effective AI implementation hinges on organization’s understanding of and accounting for these obstacles.More than half of machine learning users today cite security and compliance as their most significant challenges with the technology. AI requires a considerable amount of data to deliver reliable results, which can make companies a target for cybercrime. The solution is to use synthetic data wherever possible to minimize privacy concerns, store AI in secure, encrypted locations, and restrict access to this data so only employees who need it for their work can view it.
Data cleanliness is another concern. AI is only as accurate as its data, so ensuring this information is error-free and in a standard format is important. Manufacturers can use automated data cleansing solutions to clean and organize their AI data before analysis to improve reliability.
AI is also expensive, so it’s best to start small. Manufacturers should first apply it to the area where they’ll benefit most. As they begin to see a return on their investment, they can expand AI to other workflows, using any lessons learned in the initial implementation.
Manufacturing AI Has Big Implications for Medical Devices
Manufacturing AI is reshaping many production workflows and medical device companies stand to gain more than most. Capitalizing on this movement starts with recognizing how AI can help manufacturers.When medical device manufacturers know how to benefit from AI and what obstacles to watch out for, they can implement it with better results. As they do, they’ll unlock their full potential and ensure they remain competitive in an increasingly tech-centric market.
Emily Newton is the Editor-in-Chief of Revolutionized. She’s always excited to learn how the latest industry trends will improve the world. She has over five years of experience covering stories in the science and tech sectors.