Medical device companies are under pressure to do more with less.
The healthcare system is focused on patient-centricity, which means having to sustain a high quality of service. Additionally, market competition between device companies is fierce, there are more regulations than years prior, and reimbursements continue to drop.
Medical device companies must meet customer expectations, continue to innovate and stay competitive while lowering costs. Artificial Intelligence (AI) can be the balm to ease some of the burdens on medical device manufacturers.
What is Artificial Intelligence?
Artificial Intelligence (AI) is training machines to learn and solve problems like humans.
What is Machine Learning?
Machine Learning (ML) is one type of AI. A computer or machine is programmed to learn on its own using historical data.
What is Deep Learning?
Deep learning is another subset of Machine learning. Deep learning algorithms are designed to process information the way the human brain works. They can handle more complexity and ambiguity.
The difference between Machine Learning and Deep Learning is how a dog learns versus a child. Through repetition, we can train dogs to improve their performance on specific tasks. In time, they can anticipate certain events ahead of time – like bringing you their leash before a walk.
Deep Learning algorithms mimic how our brains work. For example, a five-year-old child can learn to use a smartphone using their natural curiosity and intelligence. Deep Learning algorithms try to make sense of the world the way the human brain does, even if data is incomplete, unstructured, or fragmented.
What is Natural Language Processing or NLP?
Natural Language Processing or NLP is another sub-category in the field of AI. NLP is software that attempts to “understand” human language and respond the way a human might. This means it learns not only the words but the intent and context behind the words.
Here’s how Artificial Intelligence, and its subsets - ML, DL, and NLP - are transforming the world of medical devices.
3D Anatomic Modeling for preoperative planning
Healthcare, in general, is moving towards personalized, patient-centric care. Advances in medical imaging are reshaping how we make medical devices.
Medical device companies can now use high-resolution CT, MRI, or XRay scans to auto-generate 3D anatomic models.
Machine learning algorithms learn from hundreds and thousands of anatomical scans and clinical datasets. This training helps them identify joint structure and disease conditions. Using this insight, medical device companies can develop accurate 3D anatomic models to optimize implant design, fit, and manufacture for every single patient. Deep learning algorithms already help physicians find disease anomalies in radiological patient scans more effectively.
When AI generates entire surgical plans, NLP can “read” a surgeon or design engineer’s notes and create accurate 3D models faster.
Together, they can help generate accurate anatomic models in a few minutes instead of a few weeks.
Benefits that AI-driven 3D anatomic modeling can bring include:
Here's an example of what AI-driven 3D anatomic modeling can do:
Take Total Knee Arthroplasty, for example. 20% of Total Knee Arthroplasty patients are still dissatisfied post-surgery. With 3D anatomic modeling, medical device manufacturers can better understand the level of wear and tear present and the quality of soft tissues in each patient’s joint. This information is valuable in designing the medical implants and instrumentation of the future.
Currently, medical device manufacturers use skilled labor to construct these digital models manually. This process can take time, cost more, and carries a higher risk of human error. AI can drive accuracy, efficiency, and innovation in implant design.
No two patients are exactly alike. 3D-printed medical devices are game-changing innovations to meet each patient’s unique needs.
How does 3D printing for medical device implants work?
3D printing uses digital scans or files for manufacturing custom-made implants, instruments, or anatomic models. It is also known as additive manufacturing because products are made by adding layers upon layers of material. This differs from the traditional manufacturing process, where layers are removed from bigger blocks of material. 3D printers can use radiological scans, computer-aided design and drafting (CAD) files, or reverse engineering.
Introducing AI to 3D printing can help device manufacturers improve their accuracy. This way, they can anticipate and reduce potential errors early in the production process. The increased productivity can help lower costs significantly.
Benefits of AI-driven 3D printing of medical devices include:
Medical device manufacturers are already struggling with inefficiencies in case coverage and inventory management across all product lines. But as patient demographics change, the number of surgical cases scheduled is on the rise. In orthopedics, the drive towards outpatient procedures for TKAs and THAs will further increase kit usage.
As a result, instrumentation must be more robust, and fewer kits will be available to rotate between hospitals and ASCs. The stakes are also higher for order fulfillment – not replacing defective or missing instruments can affect entire surgery schedules.
With fierce competition in the marketplace, medical device manufacturers must also innovate and get products to market faster. Each product line brings additional SKUs and accompanying surgical instruments. Companies with intelligent inventory control can rise above the rest to satisfy their customers, stay agile, and keep costs down.
AI-powered predictive analytics can transform how medical device manufacturers manage their inventory.
Benefits of AI for medical device inventory management include: