Conclusions Declaration of competing interest Acknowledgment References Figures (7) Tables (7) Table Table 1 Table Table 2 Table Table 3 Table Table 4 Table Table 5 Table Table 6 Highlights • A systematic review under both scientometric and qualitative analysis for the topic is provided. • A scientometric review is performed on 4,473 journal articles published in 1997–2020. • AI’s main benefits including modeling and pattern detection, prediction, and optimization • Special concerns have been put on six state-of-the-art applications of AI in CEM. • Key directions of future studies, like smart robotics, digital twins, and blockchains are provided. Then, a brief understanding of CEM is provided, which can be benefited from the emerging trend of AI in terms of automation, risk mitigation, high efficiency, digitalization, and computer vision . Special concerns have been put on six hot research topics that amply the advantage of AI in CEM, including (1) knowledge representation and reasoning, (2) information fusion, (3) computer vision, (4) natural language processing , (5) intelligence optimization, and (6) process mining.

Inter-firms networking can have different forms (Hadjimanolis, 1999 ; Kaufmann and Todtling, 2002 ; Doloreux, 2004 ), and the inter-organizational linkages of this collaboration could be vertical, horizontal, or lateral (Hadjimanolis, 1999 ). Different types of partners have other effects and results on the innovation process (Tether, 2002 ). For example, some studies suggest that cooperation forms with ones located downstream and upstream in the value chain are conducive to embodied innovation spillover through commercial trading and the value-adding process (Rao, 2001 ). Organizational learning plays a fundamental role in generating innovation and even more when different players interact, promoting knowledge sharing (Fang and Zou, 2010 ; Roberts et al. , 2012 ). Some exciting research underlines the value of the well-known concept of absorptive learning capability (Cohen and Levinthal, 1990 ) and the joint learning capability, two different types of learning capacity, in enhancing the creation of Innovation (Clauß, 2012 ; Choi et al. , 2019 ). In the healthcare industry, characterized by complexity and uncertainty, partners need to create a shared vision to take advantage of opportunities for change, encouraging innovative practices while managing risk (MacLeod et al. , 2019 ). It is exciting to reflect on the different actors of the buyer-supplier relationship in generating and adopting the innovation. An exploratory case study in the healthcare ecosystem ”, Journal of Business Research , Vol. 129 C , pp. 849 -859 . Lynn , M . and Gelb , B.D . (1997 ), “Identifying innovative national markets for technical consumer goods ”, International Marketing Review , Vol. 13 No. 6 , pp. 43 -57 . MacLeod , M.L. , Hanlon , N. , Reay , T. , Snadden , D. and Ulrich , C. (2019 ), “Partnering for change: how a health authority, physicians, and communities work together to transform primary healthcare services ”, Journal of Health Organization and Management , Vol. ahead-of-print No. ahead-of-print . Makridakis , S. (2017 ), “The forthcoming Artificial Intelligence (AI) revolution: its impact on society and firms ”, Futures , Vol. 90 , pp. 46 -60 . Mancinelli , S. and Mazzanti , M. (2009 ), “Innovation, networking and complementarity: evidence on SME performances for a local economic system in North-Eastern Italy ”, The Annals of Regional Science , Vol. 43 No. 3 , pp. 567 -597 . McHugh , M. and Brotherton , C. (2000 ), “Strategic warning: powerful buyers can seriously damage organisational health ”, International Journal of Management and Decision Making , Vol. 1 No. 1 , pp. 28 -43 . McHugh , M. , Humphreys , P. and Mclvor , R. (2003 ), “Buyer-supplier relationships and organizational health ”, Journal of Supply Chain Management , Vol. 39 No. 1 , pp. 15 -25 . Meskó , B. , Hetényi , G. and Győrffy , Z. (2018 ), “Will artificial intelligence solve the human resource crisis in healthcare? ”, BMC Health Services Research , Vol. 18 No. 1 , p. 545 . Min , H. (2010 ), “Artificial intelligence in supply chain management: theory and applications ”, International Journal of Logistics: Research and Applications , Vol. 13 No. 1 , pp. 13 -39 . Montes , G.A. and Goertzel , B. (2019 ), “Distributed, decentralized, and democratized artificial intelligence ”, Technological Forecasting and Social Change , Vol. 141 , pp. 354 -358 . Nieto , M.J. and Santamaría , L. (2010 ), “Technological collaboration: bridging the innovation gap between small and large firms ”, Journal of Small Business Management , Vol. 48 No. 1 , pp. 44 -69 . Omachonu , V.K. and Einspruch , N.G. (2010 ), “Innovation in healthcare delivery systems: a conceptual framework ”, The Innovation Journal: The Public Sector Innovation Journal , Vol. 15 No. 1 , pp. 1 -20 . Oumlil , A.B. and Williams , A.J. (2011 ), “Strategic alliances and organisational buying: an empirical study of the healthcare industry ”, International Journal of Procurement Management , Vol. 4 No. 6 , pp. 610 -626 . Plsek , P. (2003 ), “Complexity and the adoption of innovation in health care ”, Accelerating Quality Improvement in Health Care: Strategies to Accelerate the Diffusion of Evidence-Based Innovations , National Institute for Healthcare Management Foundation and National Committee for Quality in Health Care , Washington, DC . Quan , H. , Li , S. and Hu , J. (2018 ), “Product innovation design based on deep learning and Kansei engineering ”, Applied Sciences , Vol. 8 No. 12 , p. 2397 . Rao , B. (2001 ), “Broadband innovation and the customer experience imperative ”, International Journal on Media Management , Vol. 3 No. 2 , pp. 56 -65 . Roberts , J. (2000 ), “Knowledge systems and global advertising services ”, Creativity and Innovation Management , Vol. 9 No. 3 , pp. 163 -170 . Roberts , N. , Galluch , P.S. , Dinger , M. and Grover , V. (2012 ), “Absorptive capacity and information systems research: review, synthesis, and directions for future research ”, MIS Quarterly , Vol. 36 No. 2 , pp. 625 -648 . Rogers , E.M . (1995 ), Diffusion of Innovations , 4th ed. , The Free Press , New York . Salema , G. and Buvik , A. (2016 ), “The impact of buyer-supplier integration on supplier logistics performance in the hospital sector in Tanzania: the moderation effect of buyers' cross functional integration ”, International Journal of Procurement Management , Vol. 9 No. 2 , pp. 166 -184 . Schiavone , F. (2020 ), User Innovation in Healthcare: How Patients and Caregivers React Creatively to Illness , Springer Nature , NY . Schiavone , F. , Sabetta , A. , Leone , D. and Chiao , B. (2021 ), “Industrial convergence and industrial crisis: a situational analysis about precision medicine during the covid-19 pandemic ”, IEEE Transactions on Engineering Management , pp. 1 -12 . Schumpeter , J. (1934 ), The Theory of Economic Development , Harvard University Press , Cambridge MA . Song , X. , Tian , H. and Wu , X. (2010 ), “Study on SMEs-oriented strategic decision support system based on competitive intelligence ”, 2010 International Conference on Management and Service Science , IEEE , pp. 1 -4 . Stefani , U. , Schiavone , F. , Laperche , B. and Burger-Helmchen , T. (2019 ), “New tools and practices for financing novelty: a research agenda ”, European Journal of Innovation Management , Vol. 23 No. 2 , pp. 314 -328 . Suanj , Z . (2000 ), “Innovative climate and culture in manufacturing organizations; differences between some European Countries ”, Social Science Information , Vol. 39 No. 2 , pp. 349 -361 . Tamayo-Torres , I ., Gutierrez-Gutierrez , L . and Haro-Dominguez , M.C . (2010 ), “Innovation and operative real options as ways to affect organisational learning ”, International Journal of Technology Management , Vol. 49 No. 4 , pp. 421 -438 . Teece , D.J. (1986 ), “Profiting from technological innovation: implications for integration, collaboration, licensing and public policy ”, Research Policy , Vol. 15 No. 6 , pp. 285 -305 . Tether , B.S. (2002 ), “Who co-operates for innovation, and why: an empirical analysis ”, Research Policy , Vol. 31 No. 6 , pp. 947 -967 . Thakur , R. , Hsu , S.H. and Fontenot , G. (2012 ), “Innovation in healthcare: Issues and future trends ”, Journal of Business Research , Vol. 65 No. 4 , pp. 562 -569 . Tubaro , P. and Casilli , A.A. (2019 ), “Micro-work, artificial intelligence and the automotive industry ”, Journal of Industrial and Business Economics , pp. 1 -13 . Tyan , J. and Wee , H.M. (2003 ), “Vendor managed inventory: a survey of the Taiwanese grocery industry ”, Journal of Purchasing and Supply Management , Vol. 9 No. 1 , pp. 11 -18 . Wehde , M.B. (2019 ), “Healthcare 4.0 ”, IEEE Engineering Management Review , Vol. 47 No. 3 , pp. 24 -28 . White , A. and Daniel , E.M. (2004 ), “The impact of e-marketplaces on dyadic buyer-supplier relationships: evidence from the healthcare sector ”, Journal of Enterprise Information Management , Vol. 17 No. 6 , pp. 441 -453 . Yu , W. , Zhao , G. , Liu , Q. and Song , Y. (2020 ), “Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: an organizational information processing theory perspective ”, Technological Forecasting and Social Change , Vol. 163 , p. 120417 . Further reading Aria , M. and Cuccurullo , C. (2017 ), “bibliometrix: an R-tool for comprehensive science mapping anal ysis ”, Journal of Informetrics , , Vol. 11 No. 4 , pp. 959 -975 . Awan , U. , Kraslawski , A. and Huiskonen , J. (2018 ), “Governing interfirm relationships for social sustainability: the relationship between governance mechanisms, sustainable collaboration, and cultural intelligence ”, Sustainability , Vol. 10 No. 12 , p. 4473 . Bowersox , D.J. , Closs , D.J. and Drayer , R.W. (2005 ), “The digital transformation: technology and beyond ”, Supply Chain Management Review , Vol. 9 No. 1 , pp. 22 -29 . Breidbach , C.F. and Maglio , P.P. (2016 ), “Technology-enabled value co-creation: an empirical analysis of actors, resources, and practices ”, Industrial Marketing Management , Vol. 56 , pp. 73 -85 . Calvelli , A. (1998 ), Scelte d’impresa e mercati internazionali: strategie, organizzazione, finanza , G.

Artificial intelligence in health care: past, present and future . Stroke Vasc Neurol . 2017 ;2(4):230 . doi:10.1136/svn-2017-000101 29507784 , [Google Scholar] ,13 Gilvary C , Madhukar N , Elkhader J , Elemento O . The missing pieces of artificial intelligence in medicine . Trends Pharmacol Sci . 2019 ;40(8):555 –564 . doi:10.1016/j.tips.2019.06.001 31277839 , [Google Scholar] ,14 Inkster B , Sarda S , Subramanian V . An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study . JMIR Mhealth Uhealth . 2018 ;6(11):e12106 . doi:10.2196/12106 30470676 , [Google Scholar] Given the technical, emotional, and legal complexities associated with the field of medicine, there is a growing concern about the effectiveness and impact of AI in medical practices.4 Gerke S , Minssen T , Cohen G . Ethical and legal challenges of artificial intelligence-driven health care In: Artificial Intelligence in Health Care . 1st ed. 2020 :295 –336 . , [Google Scholar] ,12 Loh E . Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health . BMJ Leader . 2018 ;2(2):59 . doi:10.1136/leader-2018-000071 , [Google Scholar] It is worth pondering, in the event of TS, what the structure would be with regard to the patient-centered rational model of health care which rests on compassion and trust. There are still too many unknowns; however, if we draw projections from the information available today, it seems that TS, when it happens, will not be absolute (ie, it will not lead to the complete removal of human intervention from medical care). Brailas et al aptly pointed out that AI and humans form a bio-techno-social system in which each participating actor coevolves.32 Brailas A . Psychotherapy in the era of artificial intelligence: therapist panoptes . Homo Virtualis . 2019 ;2(1):68 –78 . doi:10.12681/homvir.20197 , [Google Scholar] The future, therefore, cannot be accurately predicted based on the capabilities and understanding of humans and society as they exist today.

AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI. big data deep learning neural network support vector machine stroke This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.

Abstract This introduction to this special issue discusses artificial intelligence (AI), commonly defined as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” It summarizes seven articles published in this special issue that present a wide variety of perspectives on AI, authored by several of the world’s leading experts and specialists in AI. It concludes by offering a comprehensive outlook on the future of AI, drawing on micro-, meso-, and macro-perspectives.

Limited data availability, coupled with biological complexity in modelling highly variable living cells, necessitates a decision support methodology that is performant under high levels of uncertainty. Wet lab experimentation on a biomanufacturing feed optimisation problem verified that inferred candidates can successfully support domain experts in designing a new optimised feed strategy with significantly higher product yield than the current industrial control strategy.