Keywords

1 Introduction

The United Nations’ sustainability goals, known as the Millennium Development Goals, have made the concept of sustainability more important than ever in recent years. To achieve sustainability the three interconnected dimensions of economic, environmental, and social must be balanced (Laguna 2014).

To ensure the well-being and development of individuals and societies, the 17 Sustainable Development Goals of the 2030 Agenda must thus be adopted by all nations since they set the global priorities for 2030 and define a course of action for the well-being of people, the planet, prosperity, and peace.

Therefore, governments must innovate their current practices in order to increase citizen participation, accountability, and interoperability in order to serve as facilitators of innovation, sustainability, and competitiveness. This could be achieved by using intelligent technology to adapt to and become resilient in the face of global change (Sharma, Yadav & Chopra 2020).

Digital technologies including artificial intelligence (AI), machine learning, smart sensors and robots, big data analytics, and the Internet of Things are being aggressively implemented by hospitals and care providers around the world, particularly in developed nations (Hee Lee & Yoon 2021). AI is the study of “intelligent agents,” which are any creatures or objects that have the capacity to see, comprehend, and behave in a way that will increase their chances of success (Rong et al. 2020).

To support the sustainable development goal of health as part of the coordinated implementation of the SDGs for health and related issues (HHSDG), this study aims to improve the understanding of the research by academics and policymakers and to inspire them to further their research on the use of AI in healthcare. The paper further provides an insight into post-Covid AI implications and lessons learned to provide better resolutions for human well-being during future pandemics.

Therefore, to gain more understanding of AI implications in healthcare to achieve SDG, this paper will explore the following research questions:

  1. 1.

    What part does AI play in the healthcare industry in achieving SDGs?

    1. 1a)

      How AI improve the implementation of health and health-related SDG?

    2. 1b)

      What are AI’s new opportunities and challenges in the healthcare industry?

The present section gives a brief outline of the idea of the paper. The second section presents the literature review with an overview of the keywords and their correlation. The third presents the methodology of the paper mentioning the sources of the literature, the search criteria, and the total number of papers reviewed and categorized under each theme. The fourth section outlines the findings and discussion of this paper. The fifth section summarizes the paper’s conclusions and recommendations for future research.

2 Literature Review

2.1 AI in Healthcare to Achieve SDG

Vinuesa et al. (2020) elaborate on the impact of AI on the 169 targets and 17 goals listed in the 2030 Agenda for Sustainable Development. The 134 targets (or 79%) across all SDGs are facilitated by AI, while 59 targets (or 35% of all SDG targets) are negatively impacted by the development of AI.

As only one of the 17 SDG goals concerns health which is SDG 3, the other goals include a variety of health variables that improve health and human well-being. As Nilsson et al. (2018) emphasize due to the interconnectedness of the SDGs, success must be ensured through integrated implementation of health and health-related sustainable development goals (HHSDG) to meet numerous healthcare targets and avoid compromises.

Thus, innovative cross-sectoral implementation strategies would be needed to achieve the SDGs, particularly SDG 3 and the HHSDGs, even though the millennium developmental goals helped many countries improve, notably in the area of health, the globe as a whole lagged in achieving the health targets. Therefore, it is the WHO’s 2018 Global Reference List of 100 Core HHSDG Indicators which serves as the foundation for the health-related SDGs that contain SDG3 and all of its targets (World Health Organization 2018).

Therefore, to have a consolidated implementation that encompasses all dimensions of health and health-related SDGs (HHSDG), Aftab et al. (2020) developed nine areas that make a framework, each of which represents a crucial step in the national planning and implementation of the HHSDGs. The nine domains conceptually represent institutional, technical, and political circumstances that may influence whether and to what extent HHSDG targets and indicators are attained with a fundamental role of governance and collaboration of organizations in different sectors. The domains are political and financial commitment, institutional setup, stakeholder engagement, the role of development partners, multi-sectorial collaboration, improving equity, capacity development, monitoring, and evaluation.

To summarize the nine domains; the majority of political support for the SDGs is articulated in the context of broader, frequently preexisting national development ambitions. Thus, governments need to utilize SDGs to attain national development objectives, improve the socioeconomic condition, and further fulfill obligations for regional development for health. This requests multi-sectoral collaboration and governance of multiagency institutions along with other active SDG stakeholders including think tanks, academia, development partners, health professionals, and civil society organizations to assess from different focal areas, such as policy guidance, financing, research, and advocacy. While SGDs are typically incorporated into existing financed development strategies and plans to ensure financial allocation. The creation of monitoring and evaluation policies, costing and budgeting for the SDGs, gender mainstreaming, technical capability, and management of statistical data, particularly administrative data, are among the key areas where requirements have been identified. In terms of equity, nations are working hard to pay close attention to the requirements of underprivileged people to secure accessible universal healthcare (Aftab et al. 2020).

2.2 AI Opportunities and Challenges in Healthcare

The growing use of AI-based technology in the healthcare sector has opened up a wide range of new options (Hee Lee & Yoon 2021). Safavi and Kalis (2019) state that it is crucial to evaluate the role that AI can play as it needs to investigate the prospects and problems related to AI applications in the healthcare sector.

AI-based technologies can significantly enhance patient care services in rural farming communities of developing countries, according to Guo and Li (2018). Additionally, this can be accomplished by increasing accuracy and reducing error. In the end, if AI can be widely used to assist such ideal healthcare, it can help ensure both high-quality healthcare and significant cost reductions. ABI Research, a marketing research consulting company, published a paper claiming that clever AI applications in the healthcare sector might result in savings of up to $52 billion in the US by 2021 (AI to Save Healthcare Sector US$52 Billion in 2021 | EPICOS n.d.) To conclude AI-based technologies can alter and develop healthcare operations to optimize, and connect with patients to boost the overall effectiveness of healthcare.

Despite its evident potential, AI does not offer a comprehensive solution. As noted by Amann et al. (2020) history has demonstrated that when technology advances, new problems and difficult tasks constantly arise. Hee Lee & Yoon (2021) agrees that as AI applications present new possibilities for enhancing people’s daily lives, they also present challenges that must be successfully overcome.Thus it is vital to adopt a multidisciplinary approach because some of these challenges are related to the technical aspects of AI while others are connected to the legal ethical, medical, and patient viewpoints (Amann et al. 2020) The stakes in the healthcare industry are particularly high since lives are on the line.

One of the most important challenges is the liability of AI use if an accident or error occurred within a patient treatment or service delivery. Given the numerous technical, managerial, and ethical considerations involved, this is an extremely challenging matter (Amann et al. 2020). Lupton (2018) stressed the importance of creating moral and ethical attitudes and behaviors for AI to benefit society.

2.3 AI Application in Healthcare in the Post-Covid Era

During COVID-19, AI technologies generally had a significant impact on reducing patients’ needs for medical care and enhancing anti-epidemic effectiveness, illustrating the vast potential of an information-driven healthcare sector (Zhang et al. 2021). The BMJ global health journal agrees that the pandemic has highlighted the strategic importance of AI and expedited its use in health.

However few societies are well-prepared for the post-COVID era as the COVID-19 pandemic is gradually brought under control through widespread vaccination. Different nations are at different stages in the process of utilizing AI technologies in the post-Covid era. Some nations have successfully included AI in their pandemic response, and they are now looking to expand its use and increase its impact more sustainably. Other nations have increased the accessibility of AI technologies and are currently looking to expand their use in specific industries. Many others are still learning about AI and are unaware of its possibilities hence growth is not linear (Get ready for AI in pandemic response and healthcare - The BMJ n.d.).

WHO (2021) is actively offering advice for two strategic stages that nations looking to improve their AI capability for health should through operational readiness and fundamental readiness. The prerequisite of Infrastructure and data support systems required to apply AI technologies is fundamental readiness. Furthermore, the key to using AI technologies responsibly and sustainably is operational preparedness.

Additionally, PWC (2020) suggests that governments must safeguard data classification, and privacy protection on data retention in terms of regulation. To ensure that the technology that supports AI is sufficient, including standards for technological integration and interoperability, cloud infrastructure, connectivity, and the creation of centralized platforms for data management through AI governance. In the long-term using the knowledge gained through COVID-19, AI can help governments be informed of the next pandemic.

3 Methodology

This study conducted systematic mapping process to allow better results and review processes overall, it further reduces bias and errors and increases the process validity due to the phase’s reproducibility during the review process (Sharma, Yadav & Chopra 2020). The main research steps in our methodology are as follows: identifying, reading, and comprehending significant publications; and systematic analysis of the discovered articles. This section breaks down the methodological approach of the paper into the following categories:

  1. (1)

    Identifying the research questions

  2. (2)

    Identify the scope and conduct the search

  3. (3)

    Screening the or relevant paper

  4. (4)

    Keywording the using abstract and inclusion and exclusion search criteria

  5. (5)

    Data extraction and mapping process

The following keywords were used to search the papers: “AI,” “Artificial intelligence,” “sustainable development,” “SDG,” “Healthcare,” and “healthcare industry” in the search engine’s title, abstract, and keywords sections. Only those papers that have been indexed by the BUiD database for high credibility, are written in English for appropriate interpretation, and match the given query are retrieved and examined; all other papers are rejected. The two databases initially produced a combined 60 records as a result of this. A list of publications for further analysis was produced after manually applying inclusion-exclusion criteria based only on abstracts and keywords and removing the duplicate articles. The study flow diagram for the research papers under examination is shown in Fig. 1.

Fig. 1.
figure 1

Systematic mapping process (Sharma, Yadav & Chopra 2020)

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4 Findings and Discussion

This paper’s aim is to examine the existing studies linked to AI in healthcare to achieve sustainable development goals in a systematic mapping approach. The mapping study’s findings would assist us in identifying and outlining potential research areas for AI for good health and well-being in the post-Covid era (see Fig. 2).

Fig. 2.
figure 2

Systematic mapping process steps (Sharma, Yadav & Chopra 2020)

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The first step of the systematic mapping process starts with defining the research question as stated previously. The second step of the systematic mapping process identifying the scope and conducting the study. To implement this step we developed a search scope to be used to gather papers relevant to the topic and keywords to avoid research bias. This process starts with identifying keywords to be used for searches within databases. The words “AI” “Artificial intelligence,” “sustainable development,” “SDG,” “Healthcare,” and “healthcare industry” are in the search engine’s title, abstract, and keywords sections. In detail, the initial process of identifying databases involves the extraction of articles with the selection of the university databases. The process of extraction of articles was completed by manual research on Google Scholar (GS) through the monitoring of citations of further relevant articles in high-ranking quality journals. The selection of journals was made based on relevance to the topics investigated. The selected papers were from Springer Link, Elsevier, Emerald, and other global journals. Additionally, journals that are known to receive documents on AI, sustainable development, and healthcare were consulted, such as the Journal of environmental research and public health, BMJ global health journal, BMC Medical informatics and decision making, greener journal of medical science, Journal of Business Research and Business Horizons.

The third stage of mapping requires screening of the relevant papers. The initial selection was based on titles and keywords and eliminating studies that did not apply to the research question. 10 papers were legitimately excluded since they were obviously outside the scope of this mapping project. However, in several instances it was challenging to infer the paper’s relevance from the title alone. In these cases, we forwarded the article to the following step for additional reading. The excluded papers with keywords “sustainable”, “robotics”, “digital health intervention”, “digital process”.

Going further with the fourth step of keywording with reading the abstract in the first stage. The next step was to build on these terms to have a more advanced understanding. Through clustering the keywords and forming titles to retrieve more accurate results such as “AI and healthcare”, “healthcare to achieve SDG” and “AI in post-Covid”. This led to excluding another 10 papers, ending up with 40 papers for this paper data extraction which is the last step of the process. As the papers that made it through the first and second stages were included along with papers that addressed research questions and were relevant to the title and inclusion keywords.

According to findings from 40 publications that were retrieved, AI largely makes it easier to accomplish SDGs. As a result, it acts as an enabler for the social, economic, and environmental pillars, directing governments and communities to prioritize its use. If opportunities are considered and risks are addressed, AI can bring about revolutionary changes in healthcare that will enhance SDGs related to health. Additional research highlighted AI as a crucial component in strategy, governance, and policy formulation to support health-related SDG’s. Innovative AI applications and technology support healthcare during and after the COVID-19 pandemic, helping to prioritize infrastructural and operational preparation as well as understanding lessons learned.

5 Conclusion and Recommendations

This paper concludes the AI mainly works as an enabler to sustainable development goals through its three dimensions social, economic, and environmental through technological enhancement. Thus, AI is a transformative tool in healthcare that can be investigated further to help attain HHSDG’s.

AI developed to serve humanity through its social duty and economic advantage, promoting universal access to affordable healthcare and assisting in the reduction of global health inequities. Additionally, AI-based healthcare applications boost the efficiency of the healthcare system while cutting costs. A less resource-constrained environment, improved innovation, and access to affordable, high-quality healthcare are all made possible by this method’s cost-effective usage of AI.

Additionally, regulations regarding confidentiality must be considered while developing AI software, and privacy and ethical standards must be developed. Also, governance should be of great attention when addressing AI implications. Standardized governance will ease policy and regulations measures globally. A main recommendation is to offer global remote training that is applicable worldwide even for low- and middle-income nations. This will improve this challenge and work toward 2030 agenda for better living.

The government can use AI to influence the three pillars of sustainable development the economy, society, and environment by implementing it in public service delivery and policymaking. However, in order to fully utilize the benefits that AI has to offer, effective applications of AI would necessitate strategies and frameworks to revolutionize the overall healthcare service. Based on papers reviewed and practical aspiration of covid-19 era there is a need for governance and collaboration of organizations in different sectors to implement a universal approach to sustainable development and necessities a policy coherence to adopt all sectors to contribute to health and HHSDG in all policy agenda. AI helps countries advance further by enabling them to concentrate on innovation, beneficial activities, and wise decision-making rather than repetitive tasks. This would ultimately help countries achieve sustainable health-related goals by enhancing quality and execution.

As the paper takes a comparative analysis approach through systematic mapping, it throws light on further research gaps and future agendas, thus portray the requirement of conducting more research on AI adoption in healthcare to implement and achieve health related SDG’s. This paper and other comparative approaches encourages further contributions related to improving global health during and post Covid era.