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\title{Artificial Intelligence in Health Policy -A Global Perspective}
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             \author[1]{Immanuel Azaad  Moonesar}

             \author[2]{Ruchi  Dass}

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\date{\small \em Received: 11 June 2021 Accepted: 2 July 2021 Published: 15 July 2021}

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\begin{abstract}
        


Introduction: Artificial Intelligence has become the new frontier for digital transformation. For healthcare, AI brings a paradigm shift, powered by increasing healthcare data availability and the rapid progress of analytics techniques globally.Objective: Several hypotheses are set forward to design a policy framework for AI technologies was discussed. This review also suggests a framework that we reflect is a better case involving "responsible AI" and "permission less innovation."Methodology: In this perspective review, AI insights into countries such as the USA, UAE, UK, and the European Union using secondary research.Results: Policy recommendations would impact multiple stakeholders in the value chain. The efficient and responsible use of AI tools would mean culture, data management, technology shifts in the industry, and required up-grading and training professionals for better coordination

\end{abstract}


\keywords{artificial intelligence, privacy laws, future of AI, health policy, responsible AI.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
n the contemporary era, many governments worldwide shifted its' policy research agenda to understand and assess the uses of social media, eservices, digital transformation, smart cities, open government data, robotics, deep learning, big data, machine learning blockchain, and artificial intelligence. The idiom "artificial intelligence" was first coined by John McCarthy at a famously held workshop at Dartmouth College, Hanover, USA, during 1956. According to John McCarthy, the father of Artificial Intelligence (AI) defined as "the science and engineering of making intelligent machines," and researchers define AI as the aim to "mimic human cognitive functions." For healthcare, AI is bringing a paradigm shift, powered by increasing healthcare data availability and the rapid progress of analytics techniques. AI generally encompasses of various activities such as machine learning, robotics, and deep learning. For the context of this perspective review, deep learning is where there are artificial neural networks. Secondly, machine learning is making machines that learn from data, such as Automatic Teller Machine cheque readers. And finally, robotics is creating devices and machines that move, such as autonomous vehicles.\par
AI has become the new edge for digital transformation. Many factors support and drive the fast and powerful evolution of Artificial Intelligence across industries. Most common amongst these are:\par
Access to sophisticated, fast, and cost-effective computing (processing) tools, hardware, and software and applications, Availability of large (big) and longitudinal data sets generated by digital efforts worldwide and technologies like IoT.\par
Availability of open-source coding resources, online communities, users (coders and managers) sharing know-how.\par
However, many companies are still struggling with real business value, and many Governments are still toying with the idea. In a nutshell, everyone wishes to weigh the risk and reward before committing to such an expensive effort. The AI business risks can around \hyperref[b0]{[1,}\hyperref[b13]{17,}\hyperref[b15]{18]} Hence, with the growing market potential and interest in AI, it is imperative to develop a thoughtthrough regulatory and legal framework on the adoption and use of AI. Several hypotheses are set forward to design a policy framework for AI technologies; the authors will discuss them. This review also suggests a framework that we think is a better case involving "responsible AI" and "permission less innovation." 
\section[{Size of the problem}]{Size of the problem}\par
As per the Grand View Research report, The global artificial intelligence market size was valued at USD 62.3 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 42.2\% from 2020 to 2027. \hyperref[b0]{[1]} AI decision-making applications that use algorithmic, neural networks, deep learning, expert and learning systems are used in education, digital imaging, healthcare, manufacturing, robotics, government, supply chain, manufacturing, and production can replace humans for a variety of processes and tasks. This dependency on automated AI-centric systems has raised enormous concern about over-allocating resources towards mitigating AI's most extreme impacts. 
\section[{II. Underlying Factors}]{II. Underlying Factors}\par
Regulations: There is an ongoing global debate on opaque AI systems, data protection regulations, and the lack of transparency on automated data processing. Regulatory approvals and interventions must have access and understanding of concrete definitions; however, the consensus around AI has been broadly worded, an elusive feat, especially in policy discussions. The United Kingdom and the European Union have already implemented AI policies that promote trustworthy AI. Europe has some stringent digital rules that are more strict than HIPAA rules in the US. For example, Article 22 stipulates that citizens cannot be submitted to medical decisions generated by an automated source. \hyperref[b1]{[2]}NIST's revised data standards have become central to AI policy under the US's Trump order. \hyperref[b2]{[3]} Policy versus Practice: AI advocates and researchers define AI that highlights its usability, functionality, and process. On the other hand, while designing Policy frameworks, policymakers recognize AI as a tool that should have caution, sensitivity, and prudence like human beings compared to human behavior. Hence, sometimes policies tend to over accentuate concern on the future use of these technologies, ignoring current usability and present-day issues. \hyperref[b3]{[4]} impact. There is consideration required to understand the destructive power of AI as well. As suggested by \hyperref[b5]{Taddeo \& Floridi (2018)}, there is a pertinent risk that the AI arms race \hyperref[b5]{[5]} can trigger inadvertent development and AI use. Hence, in addition to Fairness, Accountability, Transparency, and Ethics, human rights serve as a complementary framework for guiding and governing AI and machine learning research and development. \hyperref[b6]{[6]} Governance: Here, we are taking the example of healthcare as an industry to understand governancerelated challenges. Healthcare, as an industry, has established processes and frameworks. The fast pace development and roll-out of AI-related projects may hamper such frameworks. Hence, to maintain such processes and frameworks, an overarching framework must assess and establish potential areas of impact and how regulations may view these changes. Innovation in processes, analysis, and research needs to be developed in the light of maintaining transparency, accountability, and social impact/public interest, as stated in the problem statement above. In addition to these frameworks, it is also essential to develop skill sets amongst the subject matter experts and the user community to plan, assess, and evaluate the best use case of AI for their respective industries. 
\section[{III.}]{III.} 
\section[{Policy Recommendations \& Implications}]{Policy Recommendations \& Implications}\par
Fig.  {\ref 1}: Recommendations on the AI policy framework Policy suggestions below would impact multiple stakeholders in the value chain. This is because efficient and responsible use of Artificial intelligence tools would mean culture, data management, technology shifts in the industry, and required up-grading and training professionals for better coordination. To achieve the promise AI technology brings in and its efficient use, these policy suggestions will form the policy framework upon which key stakeholders collaborate. The key factors and elements crucial for informing policy with sufficient evidence include collaboration, facilitation, oversight management, quality structure, education, benchmarking and best practices, ethics and accountability and 'responsible AI.  {\ref '} Given the risk imposed with the advancement and uptake of AI amongst industries, here are seven high-level recommendations summed up in Figure  {\ref 1}:\par
Collaboration: AI development and implementation should involve multi-stakeholders to collaborate for social, economic, ethical, and legal implications of AI. Public funding should be provided wherever possible to drive mandates for such collaborations nationally and internationally. Collaborations and partnerships should promote knowledge sharing, building access to information, and innovation. Hence, policymakers need to collaborate with AI experts and researchers to design and implement frameworks that facilitate research initiatives and are aligned with the technical practice of AI gaping the divide between policy and practice. Facilitation: Involvement of experts and relevant stakeholders in discussing challenges and possible safeguards against threats. Both Public and Private sectors should pool inappropriate funding for the R\&D efforts pertaining to AI. All parties (regulatory and industry stakeholders) should come together to provide access to resources that help facilitate digitization, building data access, and encouraging incentives like tax credits for both profit and non-profit research that prioritizes transparency and evidence-based validation. Policy frameworks should enable data access by creating a cooperation culture among policymakers, experts, technology users, and the general public. Oversight management: Safety and efficacy of AI are contingent upon well-thought-out risk management approaches and processes to align standards and drive compliance. "What the eye doesn't see, and the mind doesn't know, doesn't exist" \hyperref[b7]{[7]}. Hence, awareness of possible misuse, abuse, and bias is necessary amongst both researchers and policymakers alike to influence norms, design, and applications, proactively analyzing and flagging potential misuse. The policy framework should highlight all actors-roles, process risks, liabilities, and incentives to highlight opacity, bias, discrimination, inefficiency, and any other negative impact (responsible disclosure).\par
Quality structure: It is vital that stakeholders understand AI risk distribution and liability while using AI tools. An ideal AI structure/ technological framework should support:\par
Guiding principles of being explainable, transparent (auditable), and fair (unbiased)\par
And augment human capabilities and maintain human well-being by being safe, ethical, and equitable (human-centric). 
\section[{Design}]{Design} 
\section[{Structure Developme nt Education}]{Structure Developme nt Education} 
\section[{Facilitation}]{Facilitation}\par
Collaborati on 
\section[{Ethics \& Accounta bility}]{Ethics \& Accounta bility} 
\section[{Oversight management}]{Oversight management} 
\section[{Best Practices}]{Best Practices}\par
Hence quality assurance should be taken into perspective while designing, developing, and deploying AI tools. Policy frameworks also need to match realworld workflows, usability principles, and end-user needs. These AI-driven systems should also solve the redundant, disjointed, and dysfunctions of the technology/ operational systems. opportunities of AI, it is also important to realize that an uneven distribution of technology and resources can hamper equitable access to AI resources. Hence, policymakers should influence investments in building AI infrastructure, training personnel, and building an engaging community of users and researchers that help demonstrate AI value leading to voluntary adoption and standards compliance. The education interventions with stakeholder involvement should also encourage keeping these frameworks up-to-date and perceptive to upcoming challenges. Ethics and Accountability: AI adoption will only progress and reach its potential if it is used ethically to protect its users (that is, humans). A digital economic policy has been adopted by almost 40 countries, including the US and the European Union. For private organizations, the personal data protection commission (PDPC), Singapore, proposed a model that guides how ethical principles can be converted into implementable practices as per the World Economic Forum regulations.\par
In 2018, the UK also mandated five principles that could become the basis for a shared ethical AI framework.\par
These include \hyperref[b8]{[8]}:\par
Development for common good 
\section[{Act with fairness and clarity}]{Act with fairness and clarity}\par
Preserve data rights and privacy of communities.\par
AI to help improve citizens' cognitive intelligence alongside artificial intelligence.\par
Should not be used to destroy or deceive human beings autonomously.\par
IV.  Education: In addition to understanding the risk and 
\section[{Responsible AI}]{Responsible AI}\par
Responsible AI is a framework that emphasizes ethical, accountable, and transparent use of AI technologies congruous with human rights, societal norms, user expectations, and organizational values. The overarching eight principles of AI ethics and reliability as adapted from the Responsible AI framework by IT tech law is mentioned in Figure 2. \hyperref[b9]{[9]} Independent non-profit bodies like AI-Global  {\ref [10]}   \hyperref[b11]{[15]}, submitted a report on the implication of AI implementation from the angle of safety and liability. As two-thirds of the value creation by AI contributes to the B2B segment, it is a call for us all researchers, academicians, business owners, governments, and industry leaders to come together and provide due consideration to ethical automation with the use of such technologies. Neural fuzzing can be used to test large amounts of random input data within the software to identify its vulnerabilities. \hyperref[b15]{[18]} Compliance to data privacy regulations (European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).\par
V. 
\section[{Conclusion}]{Conclusion}\par
The review can influence policymakers and stakeholders to develop AI and data privacy policies and guidelines across countries globally in healthcare facilities, especially during the current drive towards the future of AI. Future research could investigate the effect of specific variables on healthcare facility users' perceptions that might influence AI use and data privacy. 
\section[{VI.}]{VI.}\par
Statements\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
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\noindent\textbf{} \par 
\begin{longtable}{P{0.8304866850321395\textwidth}P{0.0031221303948576673\textwidth}P{0.0015610651974288336\textwidth}P{0.0070247933884297524\textwidth}P{0.007805325987144169\textwidth}}
\multicolumn{5}{l}{Compliance-Does the design comply with industry}\\
regulations?\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Governments, private businesses, and non-}\\
\multicolumn{5}{l}{governmental organizations across the Middle East}\\
\multicolumn{5}{l}{region are recognizing the shift globally towards AI and}\\
\multicolumn{5}{l}{advanced technologies. PWC [13] estimates that the}\\
\multicolumn{5}{l}{Middle East is expected to ensue 2\% of the total global}\\
\multicolumn{5}{l}{benefits of AI in 2030, which is equal to US\$320 billion.}\\
\multicolumn{5}{l}{The UAE's national program on Artificial}\\
Intelligence\tabcellsep aims\tabcellsep at\tabcellsep enhancing\tabcellsep Government\\
\multicolumn{5}{l}{performance and efficiency. Recently, the Government}\\
\multicolumn{5}{l}{of Dubai, Smart Dubai, published Dubai's Ethical AI}\\
\multicolumn{5}{l}{Toolkit. The toolkit has been created to provide hands-}\\
\multicolumn{5}{l}{on support across a metropolitan ecosystem. It}\\
\multicolumn{5}{l}{supports academia, industry, and citizens in}\\
\multicolumn{5}{l}{understanding how AI systems can be utilized}\\
\multicolumn{5}{l}{responsibly. It comprises principles and guidelines and}\\
\multicolumn{5}{l}{a self-assessment tool for developers to assess their}\\
\multicolumn{5}{l}{platforms. [14] Europe's Communication on Artificial}\\
\multicolumn{2}{l}{Intelligence, 2018}\tabcellsep \tabcellsep \tabcellsep \\
Explainability-what was predicted and how "x" was\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
predicted?\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Fairness-Is it ethical or unfair to a particular group?\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Robustness-Can the model be fooled? How robust is\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
the model?\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.24526270456503016\textwidth}P{0.2152454780361757\textwidth}P{0.37045650301464256\textwidth}P{0.009517657192075797\textwidth}P{0.009517657192075797\textwidth}}
Drawbacks\tabcellsep Ethical challenge\tabcellsep \multicolumn{2}{l}{Implementation considerations}\\
1 AI Black box\tabcellsep Unexplained Predictions\tabcellsep \multicolumn{2}{l}{Build Transparency}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{a) Transparent}\tabcellsep design,\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Interpretable output-Develop}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{decision or prediction model}\\
Though AI algorithms can learn\tabcellsep \tabcellsep \multicolumn{2}{l}{with its explanation.}\\
from massive amounts of data and internalize them to make decisions, these algorithms could be a black\tabcellsep Predictions and decisions without reasons\tabcellsep \multicolumn{2}{l}{b) Model Inspection-> Model explanation->Outcome explanation}\\
box to even their creators. [16]\tabcellsep \tabcellsep \multicolumn{2}{l}{c) Use what can be explained.}\\
\tabcellsep \tabcellsep Treat\tabcellsep self-learning\tabcellsep neural\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{networks and solutions with}\\
\tabcellsep \tabcellsep care.\\
2 Algorithmic Complexity\tabcellsep Difficult to understand and comprehend the "how?"\tabcellsep \multicolumn{2}{l}{Provide adequate training \& Validate models}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Training is required for the end}\\
There is more emphasis on models to give smart decisions than ethical ones.Technical secrecy and complexity can be deception\tabcellsep Little understanding or skills around comprehending the algorithm, its functional elements, modus operandi, and relationship across system may blind decision making\tabcellsep \multicolumn{2}{l}{professional to interpret and explicably understand the AI models and the application. Enough test cases (vertical domain) should be run to validate}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{the results of AI.}\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 		 		\backmatter   			  			  			  				\begin{bibitemlist}{1}
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