The Augmented Age: A New Era for Trauma and Orthopaedics

Over the course of human history, there have been four major eras defined by the way we work. The Hunter-Gatherer Age lasted several million years, followed by the Agricultural Age, which lasted several thousand years. The Industrial Age lasted a couple of centuries, and the Information Age lasted just a few decades. Today, we are on the cusp of a new age in human history - the Augmented Age. In this new era, our natural human capabilities will be augmented by computational systems that help us think, robotic systems that help us make, and a digital nervous system that connects us to the world far beyond our natural senses.

Cognitive Augmentation

The first area of augmentation is cognitive. We are already seeing the early stages of cognitive augmentation with tools like Siri, which allow us to quickly and easily find information. But this is just the beginning. In the Augmented Age, we will see a leap from passive tools to generative ones. Generative design tools use algorithms to synthesise geometry and come up with new designs all by themselves. All they need are our goals and constraints.

For example, in the case of an aerial drone chassis, all you would need to do is tell the computer that you want it to be as lightweight as possible, aerodynamically efficient, and have four propellers. The computer then explores the entire solution space, creating millions of possible solutions that meet your criteria. The computer then presents designs that we could never have imagined on our own. The exciting thing is that we are starting to see this technology used in the real world. Airbus, for example, has been working with generative-design AI to create 3D-printed cabin partitions that are stronger than the original yet half the weight.

While computers can now generate their own solutions to well-defined problems, they still have to start from scratch every time. They never learn like humans do. We are, however, getting closer to creating AIs that can learn. Computer scientists have been trying to create AIs that can learn for over 60 years. While early attempts were rudimentary, we have seen recent breakthroughs such as DeepMind's AlphaGo, which beat the world's best human at the game of Go, the most difficult game we have. In order to win, AlphaGo had to use reasoning to overcome its human opponent.

Robotic Augmentation

The second area of augmentation is robotic. In the Augmented Age, we will see robots that can not only do repetitive tasks but also tasks that require creativity and problem-solving skills. Robots will become more intelligent, and we will see the development of collaborative robots, or "cobots," that can work alongside humans safely and effectively.

For example, robots can help in the field of construction. A robot can be programmed to lay bricks more quickly and accurately than a human can. This not only saves time but also reduces errors, making buildings safer and more structurally sound. Cobots can also help with tasks such as assembling electronic devices, where their precision and speed can greatly improve efficiency.

Digital Augmentation

The third area of augmentation is digital. In the Augmented Age, we will see the development of a digital nervous system that connects us to the world far beyond our natural senses. The Internet of Things (IoT) is a prime example of this. IoT devices, from smart homes to smart cars, are becoming more prevalent in our daily lives.

With IoT devices, we can remotely monitor and control our homes, vehicles, and even our health. For example, a smart home can adjust the temperature and lighting based on our preferences and habits. A smart car can monitor our driving habits and provide feedback on how to?

We are already augmented in many ways. For example, we can use our smartphones to access information instantly. We can ask Siri or Alexa a question, and get an answer in seconds. However, this is just the beginning. Our tools have always been passive - they only do what we tell them to do. But now, we are seeing a shift towards generative design tools that use algorithms to synthesize geometry and come up with new designs all by themselves. These tools can explore the entire solution space and come up with designs that we never could have imagined on our own.

Learning and Intuition

However, these generative design tools are not intuitive - they still have to start from scratch every single time. Unlike humans and animals, they never learn. This is where AI comes in. Computer scientists have been trying to get AIs to learn and reason for the last 60 years, with varying degrees of success. In 1952, they built a computer that could play Tic-Tac-Toe. In 1997, Deep Blue beat Kasparov at chess. In 2011, Watson beat two humans at Jeopardy, which is much harder for a computer to play than chess. And just a few weeks ago, DeepMind's AlphaGo beat the world's best human at Go, which is our most difficult game.

These AIs are able to learn and reason by analyzing large amounts of data and finding patterns. They can then use these patterns to make predictions and solve problems. This is similar to how humans and animals learn - by paying attention, remembering what happened, and creating patterns in their minds.

The Future of Work

The Augmented Age will change the way we work in many ways. For example, we may see a shift towards jobs requiring more creativity and critical thinking, as these skills take more work for AIs to replicate. We may also see a rise in remote work, as technology allows us to work from anywhere in the world. And we may see a greater emphasis on lifelong learning, as we need to constantly adapt to new technologies and ways of working.

However, some challenges come with the Augmented Age. For example, there is a risk that AIs will replace human workers in certain jobs. This could lead to a loss of jobs and widen the wealth gap. 

There are several potential uses for AI and machine learning in orthopaedics. Some of the most promising include:

1. Predicting patient outcomes: AI algorithms can be trained to analyse a patient's medical history, imaging studies, and other data to predict the likelihood of a successful outcome for a particular procedure. This can help surgeons make more informed decisions about treatment options and help patients understand the risks and benefits of different approaches.
2. Improving surgical planning: Machine learning can analyse CT or MRI scans to create detailed 3D models of bones and joints, allowing surgeons to plan procedures more accurately and effectively. This can help reduce the risk of complications and improve surgical outcomes.
3. Developing personalised treatment plans: AI algorithms can analyse patient data to develop customised treatment plans that consider the patient’s needs and circumstances. This can help improve patient outcomes and reduce the risk of complications.
4. Improving implant design: Machine learning can be used to analyze large datasets of patient outcomes to identify patterns and trends that can be used to improve the design of orthopaedic implants. This can lead to more effective and longer-lasting implants that are better suited to individual patients' needs.
5. Enhancing rehabilitation: AI and machine learning can be used to develop personalized rehabilitation programs that take into account a patient's individual needs and progress. This can help patients recover more quickly and with fewer complications.
6. Identifying at-risk patients: Machine learning algorithms can be used to identify patients who are at higher risk of complications or poor outcomes, allowing surgeons to take steps to mitigate these risks.
7. Analysing clinical data: AI and machine learning can be used to analyse large datasets of clinical data to identify trends, patterns, and insights that can inform research and clinical practice. This can help improve our understanding of orthopaedic conditions and treatments and improve patient outcomes.

Overall, the potential uses of AI and machine learning in orthopaedics are vast and varied. As these technologies continue to develop, they will likely become increasingly important tools in orthopaedics, helping surgeons provide better care and improve patient outcomes.