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Firstcoined in 1920, in a play by Karel Čapek, the term robot was used to denote a fictional humanoid. The first modern programmable robot was invented by George Devol in 1954, entering service on the General Motors production line in the early sixties.
Today, robots come in all shapes, sizes and applications and are used in an increasing range of medical products and services.
Drones are delivering chemotherapy medicine; diagnostic nanomachines can monitor internal body organs; and research shows opportunities such as artificial antibodies, artificial red and white blood cells and antiviral nanobots. In some care homes, robots are used to help reduce loneliness and improve mental health.
One of the most mature uses of robots in medicine is in Robotic Assisted Surgery (RAS), which allows doctors to perform many types of complex procedures with more precision, flexibility and control than possible with conventional techniques. The first robot-assisted operation on a living patient took place on March 3, 1997, in Belgium, by Dr Jaques Himpens.
Today, we have many systems on the market; and their use is widespread, including applications in cardiology, urology, gynaecology, thoracic and general surgery. The Royal College of Surgeons, in the ‘The Future of
Surgery’ report, highlighted that the public was willing to engage with robotics if this meant better access to healthcare with faster and more accurate diagnosis and treatment.
A recent report by the ABHI Robotics Assisted Surgery Group has outlined the benefits of RAS versus conventional surgery, which include reduced trauma to patients that can lead to quicker recovery and better experiences, fewer days spent in the hospital and less physical stress on surgeons, potentially extending their working life. The impact of RAS on operational efficiencies within hospitals will continue to develop as technology adoption accelerates.
The NHS is struggling with key waiting times, standards unmet for several years and longer waiting times for patients. Prof. Powis (National Medical Director of NHS England) says that robotic surgery will play a pivotal role in reducing patient waits for surgery as we go forward and we continue to see significant investment in the technology across the United Kingdom.
current generation shows us constant developments in artificial intelligence (AI). However, industries must invest in research to gain confidence in this technology and ensure deployment.
Our time during the pandemic has shown us all the power and potential of AI and robotics, from customer service chatbots to having your shopping delivered by a robot. As we continue to see huge leaps in the development of these areas, the question now is: what’s next?
Predicting the future is hard. But it is clear that the pandemic demonstrated the powerful impact and opportunities AI, robotics and automation can offer as key drivers of operational efficiency and productivity, which is critical in ensuring future economic growth.
So, how do we get the future right? The great news is the UK is already in a position to draw on its strengths across research, academia and industry. However, we also need to make progress in key areas to get this right.
The UK is a global leader in AI and third in the world for private venture capital investment in AI companies. Solidifying this position means ensuring the right skills and talent AI companies need to grow and scale.
Any AI regulation must also be workable, proportionate and clear, to give developers and adopters maximum confidence. And let’s not forget that policymakers, industries and regulators need to continue to work together to spur innovation through testbeds, sandboxes and cutting-edge research.
In robotics, more work is needed on deployment. According to research by the National Federation of Robotics, the UK is the only G7 country with a ‘robot density’ lower than the global average. Given the potential for automation to support industries, such as manufacturing, we must find ways to encourage greater deployment while also recognising and understanding real concerns about its impact on jobs and the future of work.
AI and robotics offer huge potential for what is currently an uncertain future. To realise the full potential of these technologies, we must build greater trust and confidence in AI, robotics and automation and bring everyone — especially employees and citizens — on this journey and leave no one behind.
Robotic surgery is enabling surgeons to carry out knee replacement operations with more accuracy and control than traditional surgery.1-5
The robotics technology allows surgeons to map out a knee in real-time during the procedure for Total Knee Replacement (TKR) and Unicompartmental knee arthroplasty (UKA), to view virtually before proceeding to the patient’s surgery.
The system makes it possible for a surgeon to quickly plan the procedure as many times as necessary. It can map out information such as how to reshape the ends of the bones and the placement of the implant.
Two leading London-based consultant orthopaedic surgeons, Simon Jennings and Tim Waters, have been using the latest robotic technology in their surgeries since 2018. They express their opinions on how it is transforming their work for the better.
The surgeons currently use the CORITM System developed and manufactured by global medical technology company Smith+Nephew.
requirements. It means that surgeons can tackle deformity or alignment issues.
Data generated from the procedures is enabling surgeons to constantly learn from the process. This way, they can calculate the effectiveness and outcomes to tailor the next procedure to be better.
Mr Waters says: “This technology is particularly useful for those patients whose operations may be complicated by deformity or previous surgeries where there may be metal in their legs that prevents traditional methods of measuring the knee.”
“As the outcome data come through, we are getting a much better idea of where to place the implant relative to the patient’s anatomy. This information will help improve the results of surgery both robotically and non-robotically assisted.”
The technology is constantly evolving and improving to meet the industry’s requirements. They can use data as feedback to the manufacturers through regular collaborations.
“I think it is improving month on month,” says Mr Jennings. “The burring tool we are now using, compared to the one we used to have, is much faster; and the ergonomics of the software changes to suit us.”
This advanced technology allows the surgeons to place Unicompartmental Knee Arthroplasty (UKA)6 implants more accurately4,5 and achieve increased ranges of movements earlier in the process.6 However, they feel that while there is plenty of evidence from their own experiences, it will take a few years for the data to show how it is improving patient experience and recovery.
Mr Jennings says: “It gives me amazing accuracy and control. You don’t need any previous scans as you can use the computer to do it in real-time; and within a couple of minutes, you can work out the whole mechanics of the knee, the alignment, the surface of the joint and the damage that is there.”
“You can assess the soft tissue tensions so that the ligaments are all balanced before you start your operation. I use it in every knee operation as I find that it’s so much better for me than the old way of doing it.”
It gives surgeons the advantage of accuracy and the ability to tailor the procedure to a specific patient’s
“At the moment, it is difficult to assess the effects it is having on patient outcomes as we don’t know yet if it’s down to the specific implant we are using or the robotic technique. But I think in another five years’ time, there will be good data that will show us,” says Mr Waters.
“I do not have concerns with the surgery itself and feel confident on the safety and effectiveness of robotic technology. Surgeons can do a really good job without a robot, but for me, the target is always to do better and this tool allows that. I do think it will become the gold standard,” says Mr Jennings.
Art generation, personal tutoring and programming code are now possible with AI – even for SMEs and startups. But what can we expect to see?
AI and machine learning (ML) are not just for big organisations. They can be used by SMEs and startups to grow their businesses.
Adam Lyth, Chief Executive of Contic, a tech consultancy that helps SMEs and startups to leverage AI, says: “AI applications that are affordable to SMEs and startups are already here.”
What AI can do now
Numerous AI programmes allow even the smallest businesses to do things often considered ‘futuristic.’
One example is AWS (Amazon web services) as its AI/ML services include a recommendation engine, sentiment analysis, voice recognition and an automated document extraction tool. The NVIDIA GauGAN2 AI Art Tool allows anyone to produce professional artwork. GitHub Copilot allows users to describe what they want and immediately suggests code to implement the solution –computers coding computers.
OpenAI has many functions including summarising text, tailoring it to suit comprehension levels, creating headlines and articles and generating study notes. It also allows for realistic images and art from a text description with DALL·E 2.
Adam says: “Currently, sectors at the cutting edge of AI are MarTech, AdTech and DevTech. But soon, we are likely to see applications that can allow you to add or remove objects from images and live videos; provide bespoke stories, TV series and movies; and boost innovative functions on social media.”
In advertising, he predicts the replacement of website chat functions with AI video calls and customised advertisements to be included in songs, TV programmes and street billboards (using facial recognition).
In politics, he forecasts the rise of AI campaign managers. In education, AI teaching assistants could provide programmes and homework customised to each child. Businesses are seen to thrive with the implementation of these types of technology.
Adam says: “AI services can cost as little as £200 – £2,000 a month, depending on how many customers you have.”
“Every business is different, so Contic provides bespoke advice to suit your needs.”
The Government has promised to make the UK a global artificial intelligence (AI) superpower. How can we build on our strengths, face the challenges, and create a reliable AI sector?
The UK excels at AI. We rank third in the world at developing AI technologies.
We saw investment grow by £9 billion between 2019 and 2021—receiving more investment in AI companies than France and Germany combined in 2021.
There is a huge opportunity for the UK to lead the way internationally in this rapidly developing technology.
However, for AI to reach its full potential, the public needs to know that these systems are trustworthy. The way to build that trust is through regulation. Done well, regulation can support and encourage innovation by creating an environment where new ideas flourish.
The Government has recognised the importance of creating this environment in its recent AI Strategy and AI Action Plan.
WRITTEN BY Eve Lugg Policy Manager for Data Analytics, Policy Connect
Through our work with the All-Party Parliamentary Group on Data Analytics, we bring parliamentarians together with businesses and academia to discuss the potential of AI.
We know that businesses are ready to grasp the opportunities AI can bring with both hands. The Government must now do the same.
The Government is due to publish an AI governance white paper later this year, setting out a clear pathway to effective AI governance.
The government is also investing £100 million in 16 new AI centres, including funding 1,000 doctorate degrees over five years across the country.
The government has taken some first steps. The Alan Turing Institute—the UK’s national data institute—has set up an AI Standards Hub, shaping standards to create trustworthy AI.
The Government is also investing £100 million in 16 new AI centres, including funding 1,000 doctorate degrees over five years across the country. This enables the nation to harness the skills of experts to develop new technologies that benefit the whole economy. But there is still more to be done.
This white paper needs to be honest about the risks and challenges that AI can bring. It needs to consider the full AI life cycle, from development through to deployment, to establish who is responsible for AI at all stages in its life. It should also advocate for a collaborative approach between developers, businesses, and regulators, to make sure everyone can safely use and benefit from AI.
The APPG on Data Analytics will be carrying out an inquiry into AI governance, working to inform the white paper and future policy developments to tackle these challenges.
Unlocking business growth with the rising use of revolutionary AI
customers with virtual agents that accurately respond to common queries, with spare capacity from contact centre staff being diverted to offer a more immediate and personalised capability to help those customers with more complex issues.
AI is also transforming the ways in which businesses are making decisions. As AI continues to produce more trustworthy data and efficient up-to-date algorithms, more businesses are choosing to trust and implement AI systems into their business strategy.
Our expertise and capabilities in AI and data governance, data modelling, data platform modernisation, MLOps and responsible AI, have enabled us to successfully shape, build and integrate AI products into our client’s ecosystems regardless of their industry. The focus spans from how data is collected, how machine learning is operationalised and, finally, how fair an algorithm is when providing a specific outcome.
With AI projects, it is not as easy as just get data, apply AI, get results. Defining the problem, ingesting the data and ensuring data quality are keys to successful outcomes.
AI serves as a platform for orchestrating well optimised business processes and interactions to work alongside humans to remove repetitive, mundane elements from their day-to-day roles. AI provides companies with ML tools to support decision-making processes, building agents that can perform tasks autonomously or developing reliable frameworks to manage AI model lifecycles.
With AI projects, it is not as easy as just get data, apply AI, get results. Defining the problem, ingesting the data and ensuring data quality are keys to successful outcomes.
Engaging consumers with AI Reply’s structure, made up of a network of highly specialised companies, is crucial to support multiple sectors and focus on AI, among other technologies, to drive enterprise strategies.
To drive business performance through AI-based automation, we have Sprint Reply. This is done through enhancing the experience of consumers and enriching the interactions of those who work with them. The company’s capabilities include virtual agents (chatbots) which can communicate across multiple channels such as voice, web and social channels. As well as the next generation metahuman interfaces to expose AI-based virtual agents to consumers – in human-like avatars to include emotion and tonality to create a more intimate experience with technology. In addition to that, the service works by augmenting conversational AI with physical robotics to provide digital concierges that can physically navigate an environment while interacting with people.
This benefits consumers through more efficient and experiential interactions and those working with customers to have more fulfilling, value-driven interactions that AI was unable to resolve independently. Our company helps customers, such as easyJet, to utilise AI to engage
Clearly defining the problem we want to solve through AI ensures focused efforts are able to deliver value quickly. For example, this could be increased market share through better targeting of customers or predicting Covid-19 propagation scenarios. Dirty data is disastrous and threatening to project success. Enterprise data is never clean and is often held captured in data silos.
Organisations struggle to move it into a centralised place.
We start with defining the problem, define data sets to solve the problem, then we clean the data and move it into a centralised space in a highly automated manner. We can then apply the power of Google Cloud or AWS technologies with its inherent AI capabilities to deliver real business value.
The future of business
AI is revolutionising how retailers operate – allowing them to make smarter decisions, boost sales, and ultimately enhance customer experience and drive retention. With the right AI tools and implementation approach, a whole new level of future business growth can be achieved.
Retail Reply has developed a robust AI framework to keep our organisation at the leading edge of the commercial use of AI and help customers exploit AI to their competitive advantage.
Reality is here for investors seeking more customisable, sustainable, and active portfolios as they take advantage of the benefits of AI to power funds.
Arabesque AI (AAI) is harnessing the power of AI technology and applying this to asset management. AAI launched its groundbreaking AutoCIO platform – which generates customised, active investment strategies at scale – last September.
Using the platform, investors can build hyper-customised active investment strategies that are powered by a proprietary AI Engine. AAI runs about USD 500 million worth of assets for a range of institutional clients.
A matter of preference
Yasin Rosowsky, CEO, says: “We developed a platform where clients — institutional investors, asset and wealth managers — utilise technology to tailor investment strategies. Hyper-customised active investment strategies mean they can, for example, overlay an investor’s risk preferences with their ESG (environmental, social, and governance) requirements together in their portfolio.”
“Our proprietary AI engine is constantly forecasting the direction of 25,000+ companies and combines that with client customisation preferences; in some respects, it’s like having a personalised investment team and portfolio manager,” Rosowsky adds.
Enabling and scaling Rosowsky explains that AutoCIO can improve the performance of asset managers by acting as an enabler and scaler within the industry.
“It’s not about downsizing the role of portfolio managers; it’s about making their jobs easier and improving the services they offer. Autonomous asset management is about being able to offer hyper-customisation at a fraction of the traditional cost. Currently in the industry, only high-net-worth individuals and institutional investors can create highly customised investment solutions, but those costs require very high AUMs so this is not accessible for most of the market.”
The future is certain Many asset managers are devising their own strategies to see how they can address client demands for customisation and how to fit this into their business models.
“Ultimately, I think there will be a lot more choice, and the vast majority will be powered by AI — there’s no question about that,” says Rosowsky.
With the clear need for innovation to tackle health service pressures, the potential of AI is enormous. We must not scale back our ambition.
TheNHS is experiencing unprecedented demand. With a staffing crisis and the continuing impact of Covid-19, data reveals significant operational challenges including the elective backlog, cancer waiting times, and demands on A&E.
The Government is being urged to increase spending on health; but with public sector net borrowing approaching an all-time high and calls to cut taxes, there is a danger of progress stalling. At the time of writing, reports of plans to cut investment in technology are emerging.
We need innovation to help us tackle operational pressures—investment in digital services and new technology must continue.
Artificial intelligence, for example, can help direct people to the right part of the healthcare system and help us manage population health.
Signposting and digital triage
quickly and safely.
Ada is a symptom checker that uses AI to help UK users seek timely healthcare. This is particularly useful for conditions like cancer where early diagnosis is key. Data analysis indicates that Ada could help patients identify their cancer risk at an earlier stage, helping people make informed healthseeking decisions which can lead to faster referral and better outcomes.
AI can reduce the burden on systems by signposting people to appropriate services and analysing self-reported information for risk.
Population health monitoring Babylon is using AI to create a sophisticated understanding of the risk profile of the populations it serves. Using rich sources of patient information, individuals and cohorts with the greatest health needs are identified to deliver targeted, clinician-led interventions to proactively improve healthcare outcomes— identifying and managing the risk before it materialises. We must not slow investment in health tech or scale back our ambitions as funding pressures bite.
AI can reduce the burden on systems by signposting people to appropriate services and analysing selfreported information for risk.
In Sweden, several regions use Visiba Care’s AI tool for triaging people and directing them to appropriate services by analysing self-reported information. With an accuracy rate comparable to a human, this tool has helped reduce unnecessary A&E admissions and helped optimise waiting lists. It is also supporting healthcare staff with clinical decision-making, freeing up resources, and addressing patients’ concerns
With clear examples of how AI can ease pressures and help patients access the care they need, now is the time to press on with our uptake of digital technologies.
Artificial intelligence (AI) is improving surgical planning, guidance and review, says Paul Mussenden, Chief Executive Officer, Cydar Medical. But its breakthroughs don’t stop there.
Q: How is AI transforming healthcare?
It’s operating in all areas of healthcare and helping join up the different stages of the care pathway. That’s because AI is very good at rationalising lots of complex data in a broad range of areas such as imaging data, diagnostic data, clinical data and genetic data — and using it to personalise healthcare for individual patients. It gives clinicians the best information and new insights to make better decisions.
Q: How does it help pre- and post-procedure?
Having an adaptable 3D map of the patient allows the surgeon to optimise the procedure plan. After the procedure, outcomes are compared versus the plan. Because it’s cloud-based, surgeons can share information easily, connecting with colleagues in other locations in real time. Collaboration and remote procedure support are no longer limited to a physical location, making it easier and more cost-effective.
Paul Mussenden Chief Executive Officer, Cydar Medical WRITTEN BY Tony GreenwayOver the last 15 years, there has been a big shift to minimally invasive procedures. For instance, in endovascular surgery, an artery near the groin is accessed with a needle, and a small tube — or catheter — is threaded through the blood vessels so that a stent can be placed in an aneurysm. Navigation can be challenging, so we’ve developed a technology that uses AI to create a 3D map of a patient’s blood vessels.
Q: How does this benefit the surgeon and patient?
The 3D map means the surgeon can see the vessels more clearly and accurately during the procedure, with information updated on a screen in real time. In the past, the only way to see the blood vessels was to inject a potentially harmful contrast dye. With this technology, that can be minimised.
Q: How will this technology evolve in the future?
Intelligent mapping is bringing a new level of healthcare capability. The technology will generate a map of the patient’s anatomy and compare it with similar previous patients so that surgeons can better predict the outcome. Soon, predictive analytics will optimise the clinical decisions for each patient based on real-world clinical data, their unique anatomy and risk factors. While the current focus is endovascular surgery, the approach will be developed for many other areas of medicine where minimally invasive, image-guided procedures are used.
AI can reduce the burden on systems by signposting people to appropriate services and analysing self-reported information for risk.
Predictive analytics will help optimise clinical decisions for each patient.
~ Catherine Davies, Director of the Digital Healthcare Council
Automation can be far more than just a way of improving efficiency or reducing an operator’s exposure to dull, dirty or dangerous operations.
sure that automation is not the first thing that you may think of when the topic of discussion is centred on sustainability. However, I believe it should be.
When specified and implemented well, automation can also be used as a way of reducing waste. Two possible ways to improve quality could be to implement automated vision or coordinate measuring machine (CMM) inspections between processes to ensure products are positioned in a repeatable manner and are still within tolerances so that the process is less likely to produce a ‘bad’ product.
I will use a machine tending process as an example. When robotics is used
for machine tending, it should be a given that the blank is loaded into the machine in a repeatable manner. However, if regular quality checks are not in place, there could be a full batch run before an out-of-tolerance machine is discovered. This may result in several parts either requiring rework or perhaps getting scrapped altogether.
Getting it right the first time By implementing the regular automatic checks, tool wear as an example can be discovered so an intervention can be undertaken much earlier in the process. The last thing anyone wants is for a part to get to the end of the process only to find that step one was out of tolerance. Finding an issue late in the production cycle could mean a product would need to be reworked or scrapped after it has
gone through several other processes, thus wasting time and energy.
There are loads of other examples where automation can be used to reduce waste. I have been involved in projects where the savings by simply reducing waste has paid for
WRITTEN BY George Thomson Sales Manager at Güdel UK, and Chairman, BARAthe automation project in a relatively short time.
So, the next time your company is discussing sustainability — why not look at the possibility of using automation as part of the solution?
If you would like to start your automation journey but don’t know where to start – I would highly recommend visiting the BARA website where we have outlined several topics under the ‘Expert Advice’ section to give initial information. While you’re there, why not register for our next Roundtable discussion?
While AI provides tremendous benefits, it can also cause harm and lead to negative incidents or generate risks that fuel anxieties.
Somealgorithms incorporate biases that discriminate against people for their gender, race or socioeconomic condition. Others have manipulated individuals by influencing their choices of what to believe or how to vote. Some AI-powered autonomous vehicles have even caused fatal accidents. These negative outcomes — or AI incidents — are as diverse in nature as the environments and industries where they happen.
Regulation and risk dominate the debate Governments around the world are addressing these risks as they look to protect citizens’ rights and democracies with safeguards. They want to ensure that AI is trustworthy and that it benefits people and the planet. Most are converging around risk-based AI policies.
The trick is to strike the right balance. Heavy regulations could be hard to enforce and stifle innovation, while light control could allow AI risks to continue and even make some situations worse. What’s more — rapid developments in AI make it hard to design policies and regulations that stand the test of time.
AI-related legislative proposals are gaining traction in many countries, with particularly strong momentum in heavyweights like the European Union, the United States, China and Brazil. The UK has proposed a regulatory framework for AI systems that is “proportionate, light-touch and forward-looking” according to Nadine Dorries, the Secretary of State for digital, culture, media and sport.
An international database for AI incidents
Much like climate action, AI knows no borders. This means no single country or economic actor can tackle AI-related risks alone. National governments must think about designing policies that are interoperable on an international scale.
Effective legislation and government policies will need facts about AI incidents and where they are materialising to guide sound decisionmaking. For the moment, governments are having a hard time agreeing on what constitutes an AI incident. While this is not the end goal — it is an important first step.
Allowing AI to develop effectively Luckily, governments and all stakeholders are coming together under the OECD AI Policy Observatory to develop standards and mechanisms for tracking AI incidents and risks of all types and origins. This will give governments a shared evidence base to help make informed decisions that protect citizens and democracies while
When specified and implemented well, automation can also be used as a way of reducing waste.
I’mWRITTEN BY Karine Perset