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DUBLIN--(BUSINESS WIRE)--The "Conversational Artificial Intelligence (AI) and Voice Cloning Market: Next Generation Enterprise Solutions by Use Case, Application, and Industry Verticals 2020 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report evaluates the market drivers and uses cases for conversational AI and voice cloning solutions to execute various business functions such as CRM. The report analyzes the core technologies used to build conversational AI and voice cloning solutions along with the potential application areas across industry verticals.

The report provides an analysis of leading company strategies, capabilities, and offerings. Forecasts include technologies, solutions, services, applications, tools, and platforms from 2022 to 2027. It also provides forecasts by deployment type, business type (enterprise, SMB, government), industry vertical, and specific applications.

Select Report Findings:

Traditional peer-to-peer communication systems consisting of emails, phone calls, text messages, and face to face meetings have hugely been disrupted with the widespread adoption of next-generation platforms such as social media, messaging apps, and voice-based assistants.

This has triggered a major paradigm shift in customer behavior to prefer these alternative communications platforms, providing omnichannel experience regardless of devices. Not surprisingly, younger people are at the tip of the spear of the adoption curve for text but also voice, video, and image sharing.

For additional market segments, a shift occurs in terms of customers' business engagement expectations when they realize they may engage over their favorite chat platform using text, voice, and video communications. Conversational AI plays a profound role here, automatically communicating with customers as if a real human being, but in actuality an authentically human-sounding, AI-powered bot.

Conversational AI leverages natural language, machine learning, and other technologies to help omnichannel engagement platforms better understand and interact with customers, providing automated and personalized experiences across any channel including web, applications, mobile, and other platforms. Businesses can leverage opportunities to automate customer service operations as well as marketing and sales initiatives.

Businesses are beginning to integrate conversational AI through voice assistants, chatbots, and messaging apps. We expect that 36% of enterprises will shift their customer support function entirely to virtual assistants by 2027. This prediction is supported by our findings that indicate most customers prefer to shop with business through chat applications. This represents a massive shift from five years ago.

Whereas conversational AI merely sounds like an actual human, voice cloning mimics a known person's voice that is distinguishable as someone that a person would believe is the real person that they know. Like basic conversational AI, it may be used with various applications and industry verticals, particularly retail and other consumer services-oriented business areas.

With voice cloning, businesses can introduce a customer familiar voice to build a long-term relationship and ensure a better customer experience. Voice cloning models are trained through some data set, typically within only a few hours of recorded speech. It also leverages AI and machine learning technologies to train models so that it may engage in natural-sounding, real-time conversations with customers.

In addition to shifting customer behaviors and expectations, there are some other factors that drive enterprise and contact service providers towards leveraging conversational AI and voice cloning solutions. Some of the factors include saving time for customer service, improving real-time accessibility, increasing efficiency, reducing customer acquisition costs, building long-term relationships, handling customer queries effectively, and reducing customer complaints.

Pandemic mitigation is expected to add a significant growth factor to the conversational AI and voice cloning market as businesses seek to automate operations and enhance worker safety as well as support governmental rules and regulations. As social distancing, remote work and operation, and massive digitization continue to grow, businesses will be more reliant on providing remote services to customers.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Conversational AI

2.1.1 What is Conversational AI

2.1.2 Conversational AI Architecture

2.1.3 Core Challenges

2.1.4 Core Principles

2.1.5 Technology Component

2.1.6 Conversational AI and Chatbot

2.1.7 Automatic Speech Recognition

2.1.8 Growth Drivers

2.2 Voice Cloning

2.2.1 What is Voice Cloning

2.2.2 Voice Cloning Architecture

2.2.3 AI Voice Cloning

2.2.4 Voice Anti-Spoofing and Fraud Detection

2.2.5 Core Challenges

2.2.6 Growth Drivers

2.3 Building Conversational AI and Voice Cloning Solutions

2.4 AI-Enabled Personalization

2.5 Enterprise and Customer Benefits

2.6 Artificial General Intelligence

2.7 Artificial Super Intelligence

2.8 Market Drivers and Challenges

2.9 Value Chain

2.9.1 AI Companies

2.9.2 Software/Platform Companies

2.9.3 Analytics Providers

2.9.4 IoT Companies

2.9.5 Connectivity Providers

2.9.6 Enterprises and End Users

2.10 Regulatory Implications

2.11 Pandemic Impact

3.0 Technology and Application Analysis

3.1 Conversational AI and Voice Cloning Technology

3.1.1 Machine Learning and Deep Learning

3.1.2 Natural Language Processing

3.1.3 Automatic Speech Recognition

3.1.4 Computer Vision

3.2 Conversational AI and Voice Cloning Application

3.2.1 Chatbots

3.2.2 Intelligent Voice Assistants (IVA) System

3.2.3 Accessibility/ Messaging Application

3.2.4 Digital Games

3.2.5 Interactive Learning Application

3.3 Conversational AI and Voice Cloning Functions

3.3.1 Customer Support

3.3.2 Personal Assistant

3.3.3 Branding and Advertising

3.3.4 Customer Engagement and Retention

3.3.5 Employee Engagement and Onboarding

3.3.6 Data Privacy and Compliance

3.3.7 Campaign Analysis and Data Aggregation

3.4 Conversational AI and Voice Cloning Use Cases

3.4.1 Healthcare and Life Science

3.4.2 Education

3.4.3 Telecom, IT, and Internet

3.4.4 Bank and Financial Institution

3.4.5 Travel and Hospitality/Tourism

3.4.6 Media and Entertainment

3.4.7 Energy and Utilities

3.4.8 Government and Defense

3.4.9 Retail and E-commerce

3.4.10 Manufacturing

3.4.11 Automotive

3.5 Cloud Deployment and Enterprise AI Adoption

3.6 Software Platform and Tools

3.7 5G Deployment and Edge Computing

3.8 Smart Workplace and Service Automation

3.9 Public Safety and Governments

3.10 Ethical Implications

3.11 Social Scam, Theft, and Call Fraud

3.12 Augmented Reality and RCS Messaging

3.13 Multilingualism

3.14 M2M Communications

4.0 Company Analysis

4.1 Acapela Group

4.2 Alt Inc.

4.3 Amazon

4.4 Aristech GmbH

4.5 Artificial Solutions

4.6 AT&T

4.7 Avaamo

4.8 AmplifyReach

4.9 Baidu

4.10 CandyVoice

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$13.2 Billion Conversational Artificial Intelligence (AI) and Voice Cloning Market, 2027: Next Generation Enterprise Solutions by Use Case,...

New Jersey, N.J., Aug 22, 2022 Computer vision is a field of AI that trains computers to capture and interpret information from image and video data. By applying machine learning (ML) models to images, computers can classify objects and react, such as unlocking your smartphone when it recognizes your face.

The global AI in computer vision market size is expected to witness significant growth over the forecast period. Factors, such as rising demand for computer vision systems in automotive applications, growing demand for emotional AI, and high demand for quality inspection and automation, are driving the growth of the AI market in computer vision.

The Computer Vision in Artificial Intelligence (AI) Market research report provides all the information related to the industry. It gives the outlook of the market by giving authentic data to its client which helps to make essential decisions. It gives an overview of the market which includes its definition, applications and developments, and manufacturing technology. This Computer Vision in Artificial Intelligence (AI) market research report tracks all the recent developments and innovations in the market.

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Some of the Top companies Influencing this Market include:Facebook, Cognex, Avigilon, Basler AG, COGNEX Corporation, Qualcomm Technologies, Inc., Allied Vision Technologies GmbH, Apple Inc., Xilinx, Intel Corporation, Teledyne Technologies, Microsoft Corporation, Google LLC, NVIDIA Corporation

Market Scenario:

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Segmentation Analysis of the market

The market is segmented on the basis of the type, product, end users, raw materials, etc. the segmentation helps to deliver a precise explanation of the market

Market Segmentation: By Type

Hardware, Software

Market Segmentation: By Application

Image Recognition, Machine Learning, Other Applications

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An assessment of the market attractiveness with regard to the competition that new players and products are likely to present to older ones has been provided in the publication. The research report also mentions the innovations, new developments, marketing strategies, branding techniques, and products of the key participants present in the global Computer Vision in Artificial Intelligence (AI) market. To present a clear vision of the market the competitive landscape has been thoroughly analyzed utilizing the value chain analysis. The opportunities and threats present in the future for the key market players have also been emphasized in the publication.

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Table of Contents

Global Computer Vision in Artificial Intelligence (AI) Market Research Report 2022 2029

Chapter 1 Computer Vision in Artificial Intelligence (AI) Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Computer Vision in Artificial Intelligence (AI) Market Forecast

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Computer Vision in Artificial Intelligence (AI) Market is Expected to Record the Massive Growth, with Prominent Key Players Facebook, Cognex, Avigilon...

Security and intelligence experts have advised President Muhammadu Buharis regime to use artificial intelligence (AI) to fight Boko Haram, bandits and other criminals terrorising Nigeria.

The experts spoke at the 15th International Security Conference and Award (ISCA), organised by the International Institute of Professional Security (IIPS), on Saturday in Abuja.

Director General of IIPS, Tony Ofoyetan, said deploying drones would strengthen the fight against insecurity.

There is need for more of technology in intelligence gathering and operational executions and the like. They need to understand the essence of inanimate intelligence, and that is actually the reason why we put on this conference, he said.

Mr Ofoyetan also advised the regime to look at the security challenges beyond the perspective of military actions. He added that the government should interrogate the possibility of international sponsorship of the various security challenges bedevilling the country.

Another security and intelligence expert, Kabiru Adamu, said, What this conference has done is to bring forward solutions using technology in managing insecurity in Nigeria. The reality is that technology is a force multiplier in a situation where you have paucity of funds, where you dont have enough personnel, then technology is the natural fallback.

He added, That is what this conference is proposing. All the papers that were presented discussed solutions around the use of technology, particularly the use of AI. Looking at the EndSARS crisis when Nigeria was caught unawares, its obvious our security agencies were not prepared for the kind of modernisation that took place in cyberspace.

The security expert added that Nigeria had no choice but to adopt technology in intelligence gathering to tackle the security challenges effectively.

Mr Adamu also called for the engagement of young people, especially experts in different aspects of cybersecurity, to support the military forces.

(NAN)

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Buhari regime advised to use artificial intelligence to fight bandits, Boko Haram - Peoples Gazette

In his superb book, Dominion, historian Tom Holland finds parallels between the early Christians and todays judgemental theorists of gender and race. Both can be called social-justice warriors, he notes. Each sees a judgement day close at hand, and each has zealots who relish their role as judge, jury and hangman. Wokeness is just one modern mania that has a distinctly religious quality. Arguably, there are two other modern religions that eclipse wokeness in their scope and ambition: environmentalism and artificial intelligence (AI).

Environmentalism expresses a desire to subordinate human development and welfare to a new, all-encompassing mission that of reducing the atmospheric concentration of carbon dioxide. An emergency or a crisis has been declared by activists, one which supposedly requires the suspension of political and moral norms. Every aspect of our lives is recast into this new moral framework.

Karl Marx recognised how religion gives society its shape and moral order. He called religion the general theory of this world, its encyclopaedic compendium, its logic in popular form, its spiritual point dhonneur, its enthusiasm, its moral sanction, its solemn complement, and its universal basis of consolation and justification. But Marx also recognised religions devotion to the idea that human beings are exceptional and unique: It is the fantastic realisation of the human essence. Religion is a form of fetishised or estranged humanism, Marx was saying.

Environmentalism turns this celebration of humanity on its head. Human activities are measured by the harm or impacts they cause to the natural order, and all human activity is therefore sinful. We ate the forbidden fruit by burning fossil fuels and by daring to increase human welfare and now we must pay. Even the UK prime minister signals his support for this philosophical belief when he describes the Industrial Revolution as a derangement of nature, or a doomsday machine.

Equally religious, and equally anti-human, is the current infatuation with AI. We are currently in the third wave of enthusiasm for AI in 65 years, during which periods of high hopes and investment in AI have been followed by periods of derision. This time, however, belief in the transformative power of AI has penetrated the policy, media and administrative classes as thoroughly as the belief in apocalyptic climate change.

Todays AI develops an idea that has been around from the start. It uses multi-layered neural networks which calculate probabilities to find statistical regularities or patterns.

The field is rife with anthropomorphic metaphors: AI is undergoing training, for example, or deep learning. But these terms are really misdirections, for the software has acquired no knowledge or understanding of the underlying data it is processing. Instead, the software has bludgeoned its way through a task using brute force producing a statistical approximation to achieve a result.

A better name for the various activities currently undertaken by AI may be heuristic software. But then this might remind us that its guesswork, and that things can go wrong. Sometimes this guesswork can be impressive. At other times it is sufficient to be useful. Often it is not, and AIs ignorance of the real world can be painful, and hilarious.

But companies selling AI software or services claim a great deal more on AIs behalf. AI is one of the most important things humanity is working on, insists Sundar Pichai, CEO of Alphabet, Googles parent company. It is more profound than, I dunno, electricity or fire, he even claimed last year.

Our political elites accept such claims at face value, because it allows them to indulge in a little vanity. They can imagine themselves taking their place alongside the boffins, as visionaries or vanguardistas, as the future sweeps in. Five years ago, I was one of over 200 people and only three from the professional media invited to give oral evidence to a House of Lords inquiry into artificial intelligence. In advance, we were given nine points on which the lords might wish to hear our views. One of these was how we would prepare the population for the sweeping changes that were to come from new developments in AI. Apparently, as journalists we were not expected to question such improbable claims. It was taken as a given that AI would soon be a smashing success.

Five years on, the hype has reached new levels of absurdity, with artistic pastiches of models, like Open AIs GPT-3 language generator, being mistaken for human-like sentience.

The political class was promised a fourth industrial revolution, but AI is conspicuously failing to deliver tangible practical results. Yes, it is becoming another useful tool in the data-analytics toolbox. But it has failed to make an impact on other key areas, such as robotics, just as sceptical robotics scientists predicted.

Not one radiologist has been made redundant by AI, the neuroscientist and author Gary Marcus pointed out recently. Marcus has argued for some time that the current approach to AI has hit a wall, and is proving to have very little use outside the IT industry. AI remains extremely crude and dumb. For his pains, he finds himself in the same boat as so-called climate deniers. And with uncanny echoes of Climategate, the AI priesthood even refuses to allow researchers like Marcus to view or test the models themselves, in case they find something wrong with them. Nevertheless, the stunts and AI is a faith that requires regular miracles get ever more spectacular.

In fact, invoking religion or magic when flogging AI is not new. The original term was a triumph of marketing. A young professor called John McCarthy, who co-edited an obscure academic journal called the Journal of Automata Studies, decided that this new branch of mathematics could use some pizazz. Automata werent sexy enough. I invented it when we were trying to get money for a summer study, McCarthy would later admit.

The appeal of being God, of artificially giving birth, was something Professor Sir James Lighthill identified as one of AIs promises. Lighthill undertook the review that cancelled most of the funding for AI in 1973. Today, DeepMind the AI subsidiary of Alphabet is a master at evoking unexpected or creative outcomes supposedly produced by its deep-learning applications, which critics refer to as Its alive! moments. These tricks work spectacularly well with journalists, who are only too willing to suspend their scepticism. Such credulity is abundant, for example, in a long cover feature in The Economist this month, which marvels at the emergent properties of an AI that border on the uncanny.

Throughout those first and second AI summers, religious claims were never far away. During the second revival of AI in the 1980s, philosopher Mary Midgley lamented how dreary and familiar all the great claims about AI sounded to her.

They promise the human race a comprehensive miracle, a private providence, a mysterious saviour, a deliverer, a heaven, a guarantee of an endless happy future for the blessed who will put their faith in science and devoutly submit to it, she wrote in a review of a 1984 book by Professor Donald Michie, one of the leading British AI academics (Michie led one of the few departments to survive the 1970s AI winter). Is it clear why I was reminded of hymn books?, asked Midgley. Michie exhibited a crude indiscriminating euphoria, she wrote, and there is no better description of his successors 50 years later they too have a liturgical quality.

What AI shares with radical environmentalism is a longing to create an external moral arbiter. With apocalyptic climate change, the planet is judging us because we dared improve our lot. In AIs Jesuit wing transhumanism man hasnt fallen, we were just awful all along. Among transhumanists, there is a revulsion toward the physical body, which decays and defines a fixed form, and also a revulsion at what is characterised as our hopeless irrationality. We have always been inferior to the machines, they argue, but those machines just hadnt been invented yet. By submitting to the machines, we become free, as Grimes 2018 single, We Appreciate Power, articulates:

People like to say that were insaneBut AI will reward us when it reignsPledge allegiance to the worlds most powerful computerSimulation: its the future.

Here the religious overtones are explicit immortality is achieved by digitising the physical and uploading it. The deeply misanthropic idea that humans are not unique, and are in fact a bit rubbish, is not a new invention of the AI evangelists, of course. It has become commonplace in fields such as neuroscience and cognitive science to argue that consciousness is a trick of the mind, that the subjective self is an illusion or a trick of the brain circuitry. Cognitive scientist and philosopher Daniel Dennett was making this case three decades ago. A parallel, materialist view is even older: the proposition that were just poorly functioning machinery was expressed by Richard Dawkins in his 1976 bestseller, The Selfish Gene, where he wrote: You, dear human, are simply a gigantic lumbering robot.

In the early 2000s, computer pioneer and technology critic Jaron Lanier recognised these two beliefs as two cheeks of the same backside a backside he called cybernetic totalism. He was dismayed that so many highly intelligent friends of his in science and technology were sympathetic to this collection of prejudices, in part or in whole. Of the six characteristics he identified of this worldview, one was that subjective experience either doesnt exist, or is unimportant because it is some sort of ambient or peripheral effect. Subjectivity has long been unfashionable among the intelligentsia, as James Heartfield identified in The Death of the Subject Explained in 2002. Twentieth-century literary fashions like structuralism, cognitive science and more recently behavioural science merely added some intellectual respectability to these prejudices.

Two decades ago, Lanier already had an explanation for the supposedly magical and emergent properties of todays AI. To make the computers look smart, we have to make ourselves stupid, he observed. It requires a curious act of self-abasement. Unfortunately, abasing ourselves is a habit to which our elites seem strangely addicted. Hollowing out what it means to be human has cleared the path for both artificial intelligence and apocalyptic environmentalism, two of the most powerful religions of the 21st century.

Andrew Orlowski is a weekly columnist at the Daily Telegraph. Follow him on Twitter: @AndrewOrlowski.

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The myth of 'artificial intelligence' - spiked - Spiked

Artificial intelligence is already used in healthcare; this first article in a three-part series on digital healthcare looks at the benefits and risks

Artificial intelligence is already being used to support advanced clinical decisions, improve the accuracy and safety of care, and plan and manage NHS resources. It can make machines do things that used to require human intelligence, and can draw on huge amounts of data to make calculations that are beyond any human being. Artificial intelligence-enabled robots are being developed to take on some nursing functions. Nurses need to examine how their own roles may be changed and advocate for patient involvement in light of emerging technologies. They will also need training and support to feel confident using artificial intelligence tools. This first article in a series on digital healthcare examines the benefits and risks of artificial intelligence.

Citation: Agnew T (2022) Digital nursing 1: exploring the benefits and risks of artificial intelligence. Nursing Times [online]; 118: 8.

Author: Thelma Agnew is a freelance health journalist.

The ministerial forward to Joshi and Morleys (2019) report, published by NHSX, provided one key reason to be excited about artificial intelligence (AI) in healthcare: put simply, this technology can make the NHS even better at what it does: treating and caring for people. AI is exciting, but what is it, exactly? This first in a series of articles about digital healthcare will discuss the benefits and risks of AI.

Increasingly, nurses are encouraged to lead and shape emerging digital technologies, to ensure the changes made are fit for purpose and ward against unintended consequences, such as increased workloads, dehumanised care and the exclusion of already marginalised groups of people. The areas that could be improved, or even transformed by AI, according to Joshi and Morleys (2019) report, include:

Despite this, it is difficult to approach, let alone lead on, technological advancements if nurses do not understand them, and AI may feel too big to grasp at times.

There are many definitions in the ever-growing literature released about AI. Joshi and Morleys (2019) report suggested that one of the most useful definitions in the field of healthcare is also the oldest; they explained that it dates from a research project in 1955 and stated that AI is the science of making machines do things that would require intelligence if done by people.

A publication by The Kings Fund namely, Mistry (2020) gave a more detailed, but still straightforward explanation, stating that AI is an umbrella term encompassing a number of different approaches where software replicates functions that have, until recently, been synonymous with human intelligence. This includes a wide spectrum of abilities such as visually identifying and classifying objects [and] converting speech to text and text to speech.

The origins of AI go back decades, so why are we hearing so much about it now? One reason is that recent developments in applied mathematics and computer science have made computers much better at reading patterns in large amounts of complex data, releasing AIs potential (Mistry, 2020). The possibilities of AI are being further expanded by machine learning, which has been defined by Mistry (2020) as a type of artificial intelligence that enables computers to learn without being explicitly programmed, meaning they can teach themselves to change when exposed to new data.

Most health staff still lack direct experience with AI technologies, as is highlighted in Nix et als (2022) report, developed by NHS AI Lab and Health Education England (Box 1). However, the increasing use of AI technologies in nursing, such as providing information for advanced clinical decision support, is thought to be inevitable (Booth et al, 2021; Robert, 2019).

Box 1. AI technologies: a strange science is about to become more familiar

A survey of >1,000 NHS staff in the UK by The Health Foundation namely, Hardie et al (2021) found that three-quarters of respondents had heard, seen or read not very much or nothing at all about AI. The survey also found:

The Health Foundation survey identified fears among health workers that AI technologies present a threat to their jobs. This has been echoed in several other studies, along with concerns about data governance, cyber security, patient safety and fairness (Nix et al, 2022).

The reservations about AI are unlikely to put the brakes on their adoption in healthcare. Nix et als (2022) report points to evidence that use of AI is accelerating, with an increasing number of AI technologies expected to be used in healthcare in the next three years. It highlights the AI roadmap report by Health Education England and Unity Insights (2021), which surveyed more than 200 AI technologies: 20% were estimated to be ready for large-scale deployment in 2022, with an additional 40% ready in the next three years.

AI = artificial intelligence

AI-enabled decision support systems potentially provide numerous benefits; for example, they have already dramatically improved the detection of sepsis (Horng et al, 2017). However, there are also risks, as AI is only as good as its data. Nix et als (2022) report warns that confidence in artificial intelligence (AI) is not always desirable when using it for clinical decision making, and nurses need to recognise when to balance it with other sources of clinical information (Box 2).

Box 2. Confidence in using AI for clinical decision making

A recent report from NHS AI Lab and Health Education England recommends:

The main recommendation from the report is to develop educational pathways and materials for all health professionals to equip them to confidently evaluate, adopt and use AI (Nix et al, 2022).

AI = artificial intelligence

AI systems that evolve themselves may reflect or reinforce societal biases (for example, racial biases) and other inequities present in the data (Obermeyer et al, 2019; Gianfrancesco et al, 2018). It is important for nurses to be involved in innovations such as AI to make sure they are developing systems in line with ethical frameworks and to advocate for patient involvement (Booth et al, 2021). There is also a risk that AI systems that perform extremely well in controlled conditions will be less impressive in the real world, and there are unanswered questions about their safety and cost effectiveness in healthcare settings (Maguire et al, 2021).

The NHS is already using AI and machine learning, at a population level, to help identify older people in local areas who are at risk of frailty and adverse health outcomes; one example of this is the Electronic Frailty Index, which draws on data that is routinely recorded by GP practices (NHS England, 2017).

Predictive analytics in electronic patient records should also, increasingly, help doctors and nurses to diagnose and treat the individual patient in front of them (HEE, 2019). AI has also played a key role in informing the governments response to the coronavirus pandemic: the launch of the NHS Covid-19 Data Store by NHSX has aided the analysis of vast amounts of data to:

AI is also central to the governments new digital health and social care plan (Department of Health and Social Care and NHS England, 2022). This includes using AI to develop new diagnostics capacity to enable image-sharing and clinical decision support... These technologies support testing close to home, streamlining of pathways, triaging of waiting lists, faster diagnosis and levelling up under-served areas

With the development of smaller and more-sophisticated electronic components, robots embedded with AI will likely become more widely used in healthcare. (Mistry, 2020; HEE, 2019). The highly influential Topol review predicted that robots would become the hardware for AI, performing manual and cognitive tasks, and freeing up healthcare staff to spend more time doing things that are uniquely human, such as interacting with patients (HEE, 2019).

This picture is complicated by the fact that robots have also been developed to provide social and emotional support to people, arguably blurring the line between machines and humans. Examples currently in use include:

A common theme in the literature on AI and robotics in healthcare is the expectation that patients, as well as staff, will receive multiple benefits from the introduction of intelligent machines, with improvements in early diagnosis, and the accuracy and safety of care (Mistry, 2020; HEE, 2019). Unlike human healthcare workers, robots never get bored or tired, are unaffected by hazards in clinical settings, such as X-ray radiation, and can endlessly repeat tasks that require precision without a drop in performance (Mistry, 2020). The potential is there for robotics to help with everything from moving patients to surgical procedures that are beyond the capabilities of surgeons (Mistry, 2020).

With the input of nurses, robots are also being developed to take on some nursing functions, including:

Developments do not mean that nurses are about to be replaced by intelligent machines, but they do suggest that nursing, as it is currently understood, will change. A 2019 study by former American Nurses Association executive vice president Nancy Robert suggested that the arrival of telehealth and smart robots in peoples homes will see nursing evolve into more of a coaching role, guiding patients to improve their health and providing continuity of care, but still being physically present at the bedside when it really matters (Robert, 2019). A nursing dean quoted in the study said they could not imagine ever choosing a robot over a human to care for them if they were dying, and stated that: Nuances in human behaviour will keep nurses on the front line of care (Robert, 2019).

It is hoped that AI and robotics will work together as assistants to nurses by, on the one hand, supporting advanced clinical decisions and, on the other, automating basic tasks that are time consuming but could be performed by someone or something else. In this vision of an AI-enabled future, machines free up nurses professionally to use their education, skills and experience (Robert, 2019). A barrier to nurses using the technology to fulfil their potential could be other healthcare disciplines resistance to nurses practising at the top of their licence (Robert, 2019).

There is also the risk that, as AI tools become more widely used in healthcare, they will influence how nurses practise, without nurses having the opportunity to influence them. A 2021 study on how the nursing profession should adapt for a digital future called for an immediate inquiry into the influence of AI on nursing practice for the next 10 years and beyond (Booth et al, 2021). The authors pointed out that the increased use of AI is bringing with it new policy, regulatory, legal and ethical issues; they called on the nursing profession to:

Robert (2019) suggests that nurses have a responsibility to ask about the data used to train AI systems they use, and ensure they have been checked for bias.

As outlined in Box 3, nurses will need training and support to feel confident in, and overcome the barriers to, using AI tools which will only work properly if the ageing technology infrastructure of the NHS improves (Joshi and Morley, 2019). The excitement about AI is justified but it is important not to get dazzled by the hype (Joshi and Morley, 2019).

Box 3. AI and robotics in healthcare: barriers and learning

Health Education Englands (2019) Topol review identified significant barriers to the deployment of AI and robotics in the NHS. These included:

Along with a code of conduct and guidance on the effectiveness of the technologies, the review called for workforce learning in three key areas:

AI = artificial intelligence.

Booth RG et al (2021) How the nursing profession should adapt for a digital future. BMJ; 373: n1190.

Department of Health and Social Care, NHS England (2022) A plan for digital health and social care. gov.uk. 29 June (accessed 29 June 2022).

Gianfrancesco MA et al (2018) Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine; 178: 11, 1544-1547.

Gould M et al (2020) The power of data in a pandemic. digileaders.com, 15 April (accessed 21 June 2022).

Hardie T et al (2021) Switched On: How Do We Get the Best out of Automation and AI in Health Care? The Health Foundation.

Health Education England and Unity Insights (2021) AI Roadmap: Methodology and Findings Report. HEE

Health Education England (2019) The Topol Review: Preparing the Healthcare Workforce to Deliver the Digital Future. HEE.

Horng S et al (2017) Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE; 12: 4, e0174708.

Joshi I, Morley J (2019) Artificial Intelligence: How to Get it Right Putting Policy into Practice for Safe Data-driven Innovation in Health and Care. NHSX.

Maguire D et al (2021) Shaping the Future of Digital Technology in Health and Social Care. The Kings Fund.

Mistry P (2020) The digital revolution: eight technologies that will change health and care. kingsfund.org.uk, 13 November (accessed 21 June).

NHS England (2017) Supporting Routine Frailty Identification and Frailty Through the GP Contract 2017/2018. NHS England.

Nix M et al (2022) Understanding Healthcare Workers Confidence in AI. NHS.

Obermeyer Z et al (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science; 366: 6464, 447-453.

Robert N (2019) How artificial intelligence is changing nursing. Nursing Management; 50:9, 30-39.

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