Cost-Effectiveness of a Locally Manufactured 3D-Printed AI-Assisted Videolaryngoscope: A Model-Based Economic Evaluation

In hospitals around the world, securing a clear airway for a patient who cannot breathe on their own is one of the most critical and time-sensitive tasks a medical team faces. This procedure, known as orotracheal intubation, involves guiding a breathing tube down the throat and into the windpipe. While doctors have long used a simple metal blade to view the throat, a more advanced tool called a videolaryngoscope has emerged as the preferred standard in many places. This device uses a tiny camera to send a magnified image of the vocal cords to a screen, making it much easier to see where the tube needs to go. This visual aid significantly increases the chance of getting the tube in on the very first try, which is vital because repeated attempts can lead to serious complications like low oxygen levels or damage to the teeth. Despite these clear benefits, the high price tag of these camera-equipped devices has kept them out of reach for many public hospitals, particularly in countries where resources are tight. When expensive equipment is unavailable, doctors are often forced to rely on older, less effective methods, putting patients at greater risk simply because the system cannot afford the better tools.

A team of researchers in Brazil set out to solve this problem by creating a new kind of medical device that combines modern technology with local manufacturing. They designed a videolaryngoscope that is built using a 3D printer, a process that creates objects layer by layer from plastic, and equipped it with an artificial intelligence system that helps the doctor identify the vocal cords in real time. Instead of importing expensive machines from abroad, they aimed to produce this device locally for a fraction of the cost. To see if this approach was viable, the researchers did not just build the device; they also ran a detailed economic simulation to compare it against the best commercially available option currently on the market. Their goal was to determine if this locally made, AI-assisted tool could perform just as well as the expensive imported version while saving the public health system a significant amount of money.

The study focused on the perspective of the Brazilian Unified Health System, which provides healthcare to millions of people. The researchers built a computer model that simulated thousands of intubation procedures over a five-year period. They fed this model with real data gathered from a usability test involving sixty doctors who practiced on high-fidelity simulators, as well as information from existing medical literature. In these simulations, the locally made device, which cost the researchers 292 Brazilian reais to produce, was pitted against a gold-standard imported device that costs 10,000 Brazilian reais. The model tracked two main things: how often the doctors succeeded in placing the tube on the first try, and the total cost incurred by the hospital for each procedure, including the price of the device itself and the potential costs of treating complications if the procedure failed.

The results of this simulation were striking. The locally manufactured device proved to be a strictly dominant strategy, a term meaning it was both cheaper and more effective than the imported alternative. In the model, the new device achieved a perfect 100 percent success rate on the first attempt, while the expensive imported device achieved a 98.3 percent success rate. Because the new device worked better, it avoided the costs associated with failed attempts, such as additional time in the intensive care unit or treatment for dental injuries. When the researchers calculated the total expected cost per patient, the locally made option came in at 145.39 Brazilian reais, compared to 172.27 Brazilian reais for the imported one. This meant that for every procedure performed, the health system would save 26.87 Brazilian reais while simultaneously improving the patient's safety.

To ensure these findings were not just a lucky guess, the researchers tested how the results held up when they changed the numbers to reflect uncertainty. They ran a thousand different simulations, varying the costs of materials, the lifespan of the equipment, and the likelihood of complications. Even in the most extreme scenarios—where the cost of making the local device was pushed to its highest possible limit and the performance of the imported device was assumed to be much worse—the local device remained the better choice. The computer model showed that there was nearly a one hundred percent probability that the locally made device would be the most cost-effective option, regardless of how the financial or clinical variables shifted. This robustness suggests that the savings are not fragile; they are built into the fundamental design of the solution.

The researchers emphasized that their cost estimates for the local device were based on the actual expenses of building prototypes in an academic laboratory, which means they did not include the extra costs that would come with mass industrial production, such as corporate overhead or regulatory fees. However, they noted that the price difference between the two devices is so vast that even if the local device became significantly more expensive to manufacture at scale, it would still likely remain the cheaper option. The study also highlighted that the artificial intelligence component of the device helps standardize performance, potentially allowing less experienced doctors to achieve results similar to those of experts, which further reduces the risk of errors.

This work represents a significant step toward making advanced medical technology accessible in resource-constrained environments. By proving that a locally produced, 3D-printed device can outperform an expensive imported one both clinically and economically, the study offers a compelling argument for changing how public health systems procure life-saving equipment. The findings suggest that relying on frugal innovation—creating high-quality solutions with limited resources—can break the cycle of dependency on expensive imports. For the Brazilian public health system, and potentially for others facing similar challenges, this approach offers a path to safer airway management without the burden of prohibitive costs. The study concludes that integrating additive manufacturing with open-source artificial intelligence is not just a theoretical possibility, but a practical, resource-saving strategy ready for wider adoption.