Artificial Intelligence: How To Get It Right

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Artificial Intelligence:How to get it rightPutting policy into practice for safedata-driven innovation in health and careArtificial Intelligence:how to get it rightHolistic guidance forthe developmentand deployment ofAI in health and careOCTOBER 2019

2 3A BOU T N HSXA BOU T T H IS RE P O R TNHSX brings teams from the Department of Health andSocial Care, NHS England and NHS Improvement togetherinto one unit to drive digital transformation and lead policy,implementation and change.Joshi, I., Morley, J.,(eds) (2019). Artificial Intelligence: How toget it right. Putting policy into practice for safe data-driveninnovation in health and care. London, United Kingdom: NHSX.NHSX is responsible for delivering the Health Secretary’s TechVision, building on the NHS Long Term Plan by focusing on fivemissions: Reducing the burden on clinicians and staff, so they canfocus on patients; Giving people the tools to access information and servicesdirectly; Ensuring clinical information can be safely accessed,wherever it is needed; Improving patient safety across the NHS; Improving NHS productivity with digital technology.Although this report has named editors, it results from thecollective effort of a great number of individuals who kindlygave up their time to contribute their thoughts, ideas andresearch. A full list of acknowledgements is provided at the endof the report. There are, however, several key organisations,and individuals who provided input without which this reportwould not have been possible. With this in mind, we would liketo thank:Tina Woods,Collider HealthMelissa Ream, Marie-Anne Demestihas and Sile Hertz,AHSN NetworkAnna Steere,NHSXDr. Sam Roberts,Accelerated Access Collaborative

4 5ContentsMinisterial Foreword67. Developing International Best Practice GuidanceGlobal Digital Health PartnershipWorld Health Organization (WHO) & International Telecommunication Union (ITU)The EQUATOR Network64646570Executive Summary101. 4178. Conclusion72565656565758585859Appendix: Case StudiesFlagship Case StudiesPrecision MedicineGenomics EnglandEMRADNon-clinical (operational) applications of AICogstackLessons from Estonia and FinlandNHS-R CommunityNHSX Mental HealthOptimamSurvey Case StudiesAdvancing Applied Analyticsaxial3DBrainPatchChief AIConcentric HealtheTraumaFirst DermForms4HealthGoogle HealthiRhythm TechnologiesKaidoKorticalLifelightMy CognitionRoche Diabetes Care PlatformSensyne HealthSentinelStorm IDVeye 394959596969798992. Where Are We Now?183. Developing the Governance FrameworkWhy you need Ethics & RegulationA Code of ConductPrinciple 7: Algorithmic ExplainabilityPrinciple 8: Evidence for EffectivenessPrinciple 10: Commercial StrategySelf-Assurance PortalMapping the Regulation JourneyOvercoming Regulatory Pain Points2626272834363637414. Clarifying Data Access and ProtectionNavigating Data RegulationUnderstanding Patient DataProtecting the CitizenData Innovation HubsData Collaboration at ScaleData Agreements and Commercial ModelsNHSX Data Framework44444647484952555. Encouraging Spread of ‘Good’ Innovation & Monitoring the ImpactWhat Does ‘Good AI’ Look Like?1. Precision Medicine2. Genomics3. Image Recognition4. Operational EfficiencyTackling Barriers to AdoptionMeasuring ImpactReal-world evaluation6. Creating the Workforce of the Future62References100Acknowledgements106

6 7Ministerial ForewordWe love the NHS because it’s always been there for us, through some of the best momentsin life and some of the worst. That’s why we’re so excited about the extraordinarypotential of artificially intelligent systems (AIS) for healthcare.Just as important, as a society we need to agree the rules of the game. If we want peopleto trust this tech, then ethics, transparency and the founding values of the NHS have togot to run through our AI policy like letters through a stick of rock.Put simply, this technology can make the NHS even better at what it does: treating andcaring for people.And while we’re clear-eyed about the promise of AI we can’t let ourselves be blindedby the hype (of which this field has more than its fair share). Our focus has got to be ondemonstrably effective tech that can make a practical difference, at scale, right across theNHS, not just the country’s most advanced teaching hospitals.This includes areas like diagnostics, using data-driven tools to complement the expertjudgement of frontline staff. In the report, for example, you’ll read about the EastMidlands Radiology Consortium who are studying Artificial Intelligence (AI) as a ‘secondreader’ of mammogram images, helping radiologists with an incredibly consequentialdecision, whether or not to recall a patient. In the near future this kind of tech couldmean faster diagnosis, more accurate treatments, and ultimately more NHS patientshearing the words ‘all clear’.AIS can also help us get smarter in the way we plan the NHS and manage its resources.Take NHS Blood & Transplant, who are looking at how AI can forecast how much bloodplasma a hospital needs to hold onsite on any given day. Or University College LondonHospitals (UCLH) who are trialling tools that can predict the risk of missed outpatientappointments.Most exciting of all is the possibility that AI can help with the next round of gamechanging medical breakthroughs. Already, algorithms can compare tens of thousandsof drug compounds in a matter of weeks instead of the years it would take a humanresearcher. Genomic data could radically improve our understanding of disease and helpus get better at taking pre-emptive action that keeps people out of hospitals.But while the opportunities of AI are immense so too are the challenges.Much of the NHS is locked into ageing technology that struggles to install the latestupdate, never mind the latest AI tools, so we need a strong focus on fixing the basicinfrastructure. That means sorting out the connectivity, standardising the data andreplacing our siloed and fragmented systems with systems that can talk to each other.We also need to make sure that staff have the skills, training and support to feel confidentin using or procuring emerging technology.To help us deliver those changes, we’ve set up NHSX, a new joint team working acrossthe NHS family to accelerate the digitisation of health and care. NHSX’s job is to build theecosystem in which healthtech innovation can flourish for the benefit of the NHS. Cruciallyit’s also been tasked with doing this in the right way, within a standardised, ethically andsocially acceptable framework.Getting these foundations right matters hugely, which is why we are investing 250million in the creation of the NHS AI Lab to focus on supporting innovation in an openenvironment where innovators, academics, clinicians and others can develop, learn,collaborate and build technologies at scale to deliver maximum impact in health and caresafely and effectively.

8 9The NHS AI Lab will be run collaboratively by NHSX and the Accelerated AccessCollaborative and will encompass work programmes designed to: Accelerate adoption of proven AI technologies e.g. image recognition technologiesincluding mammograms, brain scans, eye scans and heart monitoring for cancerscreening.h Encourage the development of AI technologies for operational efficiency purposese.g. predictive models that better estimate future needs of beds, drugs, devices orsurgeries. Create environments to test the safety and efficacy of technologies that can be usedto identify patients most at risk of diseases such as heart disease or dementia, allowingfor earlier diagnosis and cheaper, more focused, personalised prevention. Train the NHS workforce of the future so that they can use AI systems for day-to-daytasks. Inspect algorithms already used by the NHS, and those being developed for the NHS,to increase the standards of AI safety, making systems fairer, more robust and ensuringpatient confidentiality is protected. Invest in world-leading research tools and methods that help people apply ethics andregulatory requirements.The following report sets out the foundational policy work that has been done indeveloping the plans for the NHS AI Lab. It also shows why we’re so hopeful about thefuture of the NHS.Matt Hancock,Secretary of StateBaroness Blackwood,Minister for Innovation

10 11Executive SummaryArtificialIntelligencecould helppersonaliseNHSscreening andtreatmentsfor cancer, eyedisease and arange of otherconditions,for example,while freeingup staff timeto spend withpatients.Artificial Intelligence (AI) has the potential to make a significantdifference to health and care. A broad range of techniques canbe used to create Artificially Intelligent Systems (AIS) to carryout or augment health and care tasks that have until now beencompleted by humans, or have not been possible previously; thesetechniques include inductive logic programming, robotic processautomation, natural language processing, computer vision, neuralnetworks and distributed artificial intelligence. These technologiespresent significant opportunities for keeping people healthy,improving care, saving lives and saving money for the pilot digitaltechnologies. It could help personalised NHS screening andtreatments for cancer, eye disease and a range of other conditions,for example. Furthermore, it’s not just patients who can benefit. AIcan also support clinicians, enabling them to make the best use oftheir expertise, informing their decisions and saving them time.This report gives a considered and cohesive overview of thecurrent state of play of data-driven technologies within the healthand care system, covering everything from the local researchenvironment to international frameworks in development.Informed by research conducted by NHSX and other partners overthe past year, it outlines where in the system AI technologies canbe utilised and the policy work that is, and will need to be done,to ensure this utilisation is done in a safe, effective and ethicallyacceptable manner. Specifically:Chapters 1 and 2 set the scene. They provide an overview of whatAI is (and importantly is not), why we believe it is important, anda detailed look at what is currently being developed by the AIecosystem by evaluating the results of a horizon scanning exerciseand our second ‘State of the Nation’ survey. This analysis revealsthat diagnosis and screening are the most common uses of AI, with132 different AI products identified being designed for diagnosisor screening purposes covering 70 different conditionsChapter 3 is an in-depth look at the Governance of AI. Building onthe Code of Conduct for data-driven technologies, it explores thedevelopment of a novel governance framework that emphasisesboth the softer ethical considerations of the “should vs should not”in the development of AI solutions as well as the more legislativeregulations of “could vs could not”. In particular it covers key areassuch as the explainability of an algorithm, the evidence generationfor efficacy of fixed algorithms, the importance of patient safetyand what to consider in commercial strategies.

12 13Chapter 4 is all about the data that fuels AI. When engaging withinnovators, regulators, commissioners and citizens on AI the onetopic that is guaranteed to come up is Information Governance(IG). Protecting patient data is of the utmost importance, which iswhy IG is crucial, but it should not be seen as a blocker to the useof data for purposes that can deliver genuine benefits to patients,clinicians and the system. This Chapter highlights how we areworking collaboratively with key partners across the system (e.g.the Accelerated Access Collaborative, the Office of Life Sciences,Health Data Research UK, Genomics England, Academic HealthScience Network) to clarify the rules of IG and streamline accessto data for good through specific programmes such as the DigitalInnovation Hubs.Chapter 5 covers adoption and spread. Considering the sometimesnegative impact the complexity of the NHS as a sociotechnicalsystem has on the spread of important innovation, it covers theactions being taken to encourage adoption. However, given thechallenges involved in the practical implementation of AI we donot want to encourage adoption for the sake of adoption, so italso covers ‘what good looks like’ and how we can monitor theimpact of the introduction of AI over time so that good stays goodfurther downstream.Chapter 6 comes back to the people of the NHS. Building onthe work of Health Education England and the Topol Review,it highlights the challenges faced by the workforce in thedevelopment, deployment and use of AI and what needs to bedone in order to ensure they have the skills that they need to feelconfident in using AI in clinical practice safely and effectively.Crucially it highlights how again we cannot do this alone and mustwork closely with national centres of data science training such asthe Alan Turing Institute.Chapter 7 goes international. Health data is not only generatedin England and the AI technologies that are trained and tested onit are not developed only in England. Instead the AI ecosystem istruly international and there is, therefore, a need for internationalcollaboration and agreement of standards, frameworks andguidance. For this reason, this chapter highlights the ongoingwork of the Global Digital Health Partnership, the World HealthOrganisation and the EQUATOR network in developing these withus as a key partner.Chapter 8 concludes with the NHS AI Lab. It brings together all theinformation included in the previous chapters to highlight why weknow that the Lab is needed and why we think it will be crucial inhelping us achieve our aims of: promoting the UK as the best place in the world to invest inhealthtech. providing evidence of what good practice looks like to industryand commissioners. reassuring the public, patients and clinicians that data-driventechnology is safe, effective and protects privacy. allowing the government to work with suppliers to guide thedevelopment of new technology so products are suitable forthe health and care system in the future. building capability within the system with In-house expertise toprototype and develop ideas. making sure the NHS gets a fair deal from thecommercialisation of its data resources and expertise.

14 151. IntroductionDr. Indra Joshi& Jessica MorleyDEF I N I T I O NDiagnosticsDespite being a well-established field of computer scienceresearch, Artificial Intelligence (AI) is difficult to define and, assuch, numerous definitions exist, including:“the designing and building of intelligent agents that receiveprecepts from the environment and take actions that affect thatenvironment”1“a cross-disciplinary approach to understanding, modelling, andreplicating intelligence and cognitive processes invoking variouscomputational, mathematical, logical, mechanical, and evenbiological principles and devices”2“the science of making machines do things that would requireintelligence if done by people”3The third definition is the oldest, stemming from the field’sfounding document “Proposal for the Dartmouth SummerResearch Project on Artificial Intelligence” (1955). However, it is themost applicable to the uses of Artificial Intelligence for health andsocial care.O P P O R T U N I T I ESIn the context of health and care, a broad range of techniques(e.g. inductive logic programming, robotic process automation,natural language processing, computer vision, neural networksand distributed artificial intelligence such as agent basedmodelling4 ) are used to create Artificially Intelligent Systems(AIS) that can carry out medical tasks traditionally done byprofessional healthcare practitioners. The number of medical orcare-related tasks that can be automated or augmented in thismanner is significant. A summary of the areas of care in which suchautomated tasks could make a difference is presented in Figure 1. ImageRecognitione.g. SymptomsCheckersand DecisionSupport ystemEfficiency DrugDiscovery Digitalepidemiology PatternRecognition Nationalscreeningprogrammes Optimisationof carepathways Greaterknowledgeof rarediseases Greaterunderstandingof casuality Predictionof Do NotAttendsP4 Medicine Prediction ofdeterioration Personalisedtreatments Preventativeadvice Identificationof staffingrequirementsFigure 1 5–12This range of potential use cases for AI in health and carehighlights the scale of the opportunity presented by AI for thehealth and care sector. This is why:1. The NHS Long-Term Plan sets out the ambition to use decisionsupport and AI to help clinicians in applying best practice,eliminate unwarranted variation across the whole pathwayof care, and support patients in managing their health andcondition.2. The future of healthcare: our vision for digital, data andtechnology in health and care outlines the intention touse cutting-edge technologies (including AI) to supportpreventative, predictive and personalised care.3. The Industrial Strategy AI Mission sets the UK the target of“using data, Artificial Intelligence and innovation to transformthe prevention, early diagnosis and treatment of chronicdiseases by 2030.”We believe that the UK can be a world leader in this area for yearsto come - a core aim of the Office for Artificial Intelligence (OAI).

16 17C H A L L EN G ESAs much as we believe in the power of AI to deliver significantbenefits to health and care, and the wider economy, we also knowthat there are significant ethical and safety concerns associatedwith the use of AI in health and care.If we do not think about transparency, accountability, liability,explicability, fairness, justice and bias, it is possible that increasingthe use of data-driven technologies, including AI, within the healthand care system could cause unintended harm.Tackling these challenges so that the opportunities can becapitalised on, and the risks mitigated, requires taking action infive key areas:1. Leadership & Society: creating a strong dialogue betweenindustry, academia, and Government.2. Skills & Talent: developing the right skills that will be neededfor jobs of the future and that will contribute to building thebest environment for AI development and deployment.3. Access to Data: facilitating legal, fair, ethical and safe datasharing that is scalable and portable to stimulate AI technologyinnovation.There aresignificantethicaland safetyconcernsassociatedwith the useof AI in healthand care.4. Supporting Adoption: driving public and private sectoradoption of AI technologies that are good for society.5. International engagement: securing partnerships that deliveraccess to scale for our ecosystem.This report sets out current and future developments in each ofthese areas, and provides the rationale for why NHSX is creatingthe new 250 million NHS AI Lab in collaboration with theAccelerated Access Collaborative (AAC). Overall the goal is tohelp the system players from innovators to commissioners, to fullyharness the benefits of AI technologies within safe and ethicalboundaries, whilst speeding up the development, deployment anduse of AI so that we can get benefits to more people - patients andstaff alike - more quickly.

18 192. Where Are We Now?Jessica Morley,Marie-AnneDemestihas,Sile Hertz,Ian Newington& Mike TrenellAs a starting point, we needed to understand the baseline that wewere working from. In order to develop useful frameworks andfocus investment, we needed to understand what is:1 year3320251024a) The current state of AI in the health and care system i.e. hype vsreality;b) The challenges faced by innovators in developing AI systems;3 years85242055c) The issues faced by policy makers and regulators in governingboth the development and deployment of AI systems in health.Two activities were carried out to get an up-to-date picture of AIsolutions that are

Artificial Intelligence (AI) has the potential to make a significant difference to health and care. A broad range of techniques can be used to create Artificially Intelligent Systems (AIS) to carry out or augment health and care tasks that have until now been completed by humans, or have not been possible previously; these techniques include inductive logic programming, robotic process .

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