The manufacturing industry faces rising pressure to innovate, but medical device manufacturers are held to higher standards than most. Questions over health care accessibility push demand for lower prices while these devices’ impact on patient well-being requires strict quality standards. Artificial intelligence (AI) in manufacturing offers a promising path forward.
As AI technology has advanced and becomes more accessible, its potential use cases in manufacturing have grown. Medical manufacturers, in particular, can benefit from these applications to overcome their imposing obstacles. The following six use cases are some of the most disruptive of these applications.
AI Features in Medical Devices
The biggest impact of AI on medical device manufacturing is integration into the items themselves. AI-capable devices can make health care more accessible, improve diagnoses and boost medical response times.
AI models can diagnose cancer with 90{8f70a6f82286a397ea7021ad8fff2990e4c07835f2e47703b7976f5a37e4ea20} accuracy, putting them on par with many human professionals. This equipment can also perform tests faster than people, helping medical professionals help more patients in less time. Consumer medical devices can also benefit from AI, as wearable tech can monitor users’ bodily functions to offer more insight into their health.
Manufacturers that capitalize on this potential could see a considerable rise in demand. As more medical devices feature AI functionality, it will become the new standard for the industry. Patients, doctors and the companies that create the devices they use will all benefit from that shift.
Optimized Manufacturing Workflows
AI in manufacturing can also help medical device producers improve their internal operations. Most organizations recognize the need for optimization, but optimizing anything requires an accurate understanding of an operation’s specific problems and how best to address them. AI can give manufacturers that understanding.
Machine learning algorithms can analyze production data like lead times, material waste, and error rates to find bottlenecks and other inefficiencies. After highlighting these areas of improvement, some AI models can go further and suggest possible improvements. AI is often better at recognizing patterns and predicting outcomes based on them, so it may offer more reliable solutions than a human analyst.
Medical device manufacturers can use this AI analysis to find areas where they waste time or materials. The increased efficiency will drive down costs for patients and hospitals. These internal improvements will make health care more accessible.
Automated Quality Control
Quality control is another ideal use case for AI in manufacturing. Medical device manufacturers must complete several steps for FDA compliance, including rigorous quality assurance measures in production. Meeting those standards with manual methods can be challenging, but AI makes it easier.
AI-enabled equipment like machine vision systems can analyze products for defects so workers don’t have to. Because AI is more accurate than humans in analysis-related tasks and machines don’t get tired or distracted, this automation makes quality checks more accurate. AI can also perform these inspections faster than employees, reducing lead times.
Another advantage of automating quality control with AI is that these algorithms can notice trends over time. They can alert manufacturers if they sense repeated defects and show where these errors stem from, informing production line changes to prevent future issues.
Streamlining Clinical Trials
Medical device manufacturers can also use AI to optimize the clinical trial process. These items must undergo clinical tests before companies can sell and advertise them. Trials can take two to three years and cost between $10 million and $20 million.
AI can make this rigorous testing phase shorter and less costly. Using devices and monitoring their efficacy still takes a while, but AI can find ideal study participants and reach out to them to reduce the time it takes to start the trial. From there, intelligent algorithms can automate data entry and ensure regulatory compliance.
AI streamlines the administrative side of clinical trials and significantly reduces the time they take. It also minimizes the risks of errors that may hinder a device’s progress and require retesting. As a result, manufacturers can bring their products to market faster and at a lower cost.
Supply Chain Optimization
AI in manufacturing can also improve the medical device supply chain. Just as AI models can analyze internal workflows to highlight inefficiencies, they can look for supply chain bottlenecks and risks. Finding these areas of improvement can help manufacturers reorganize their supplier networks to become more resilient.
Machine learning is also a useful tool for modeling disruption and risk. Medical device companies that gather enough data on their supply chain can use AI to create a digital twin of the network. This virtual representation can simulate various disruptions to show how resilient it is so manufacturers know what to change if necessary.
This data can also feed into predictive AI models to inform manufacturers of supply shortages or other disruptions before they happen. Companies can respond by increasing their safety stocks or making other changes to minimize the impacts.
Faster Research and Development
AI is enabling faster research and development in medical device production. The process for a Class 2 medical device can cost $2 million to $5 million, which doesn’t include research and discovery costs. AI can streamline things by highlighting areas to target and enabling rapid prototyping.
AI models can analyze the current market to find unmet patient needs. Manufacturers can use this information to develop new devices that excel where current offerings fall short. They can then capitalize on an underserved market, ensuring better sales performance and improving patient outcomes.
AI tools can also assist in development by suggesting product designs or highlighting potential improvements in existing ones to streamline prototyping. Manufacturers can then achieve a faster time to market, improving their ROI.
AI in Manufacturing Has Far-Reaching Impacts
AI in manufacturing is still a relatively new phenomenon, so additional applications will likely emerge. This technology will become an industry standard as more manufacturers capitalize on these existing and upcoming use cases.
Medical device manufacturers can become more efficient, accurate and resilient through AI implementation. The patients they serve will be able to access better care for lower prices as a result. Given these far-reaching impacts, broader AI adoption will improve the industry for all involved.
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