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.
Difference Between Artificial Intelligence, Machine Learning, Deep Learning, and NLP?
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.
Next-Generation 3D Anatomic Modeling and Implant Design With AI
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:
- More accuracy: With AI, segmentation is refined and can lower the risk of human error and variability.
- Greater Innovation in implant design: Anatomically accurate 3D models can help surgeons and design engineers test and perfect new and intricate implant designs, even before the first cadaver lab.
- Reduced inefficiencies in the design process: It can take a few minutes to create AI-generated anatomic models. This process is even more efficient when an entire surgical plan is created by an AI, which then serves as a seamless foundation for the 3D modeling.
- Higher surgical precision: AI can help improve intraoperative accuracy with customized instrumentation. This is required, in particular, for minimally invasive surgery and to access hard-to-reach regions of a patient’s anatomy.
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.
3D Printed Implants: The Future of Additive Manufacturing is AI
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:
- Enhanced precision: AI can generate more accurate 3D anatomic models from digital files. This can help additive manufacturers fine-tune their design processes and tolerances.
- Reduced material wastage: By continually learning, AI algorithms can predict and forecast material requirements and resource requirements ahead of time. AI can optimize the entire additive manufacturing process, right from the design stage.
- Lower manufacturing costs: Lower manufacturing costs: AI can monitor and track quality, flag potential issues ahead of time. Using this data, it can recommend solutions, and optimize how designs convert to a manufactured product.
AI is Revolutionizing the World of Medical Device Inventory Management
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:
- Faster order fulfillment: From order receipt to fulfillment, there are many redundancies in the process. Missed communication (with customers and between internal teams), potential backorders, case scheduling changes, item disputes, audits, and more factors that impact order fulfillment. With predictive analytics, operations managers can plan and forecast better and streamline the entire process.
- Greater last-mile visibility for instruments: It can be hard to track the status and needs of inventory consigned at a customer location. In particular, when the number of surgeries being performed is snowballing every day. AI algorithms can read handwritten charge sheets and generate a bill of materials. Images of instrument and implant trays in the field can automatically account for product usage and help plan for the future.
AI offers intelligent tracking of instrument usage, broken or missing parts and helps companies plan better kit replenishment. With real-time visibility into usage, medical device companies get more details about surgeon order histories. This information is valuable for future planning.
- Advanced Analytics and Reporting: AI-driven reporting can offer an incredible level of granularity. It can help improve on-ground kit usage, reduce inventory loss, enhance forecasting, quality control, and fulfill regulatory requirements to account for every single medical device implant and instrument.