La transizione intelligente: la produzione industriale e il settore sanitario nel XXI secolo
DOI:
https://doi.org/10.82015/NNR.2025.100102Parole chiave:
Intelligenza Artificiale; Industria, Innovazione; Competitività; Digitalizzazione.Abstract
La rapida evoluzione della tecnologia e delle sue interconnessioni ha prodotto le formazioni dell’Industria 4.0 e della Medicina/Salute 4.0 in cui l’intelligenza artificiale (IA) interviene con finalità di monitoraggio, remotizzazione, automazione e potenziamento.
In queste pagine, la progressiva integrazione delle tecnologie intelligenti nelle strutture organizzative viene ricondotta entro il concetto scientifico di transizione socio-tecnica, presentata come una sotto-transizione della digitalizzazione e se ne discute lo stato di avanzamento. Il contributo, inoltre, fornisce un'ampia e dettagliata analisi della ricerca condotta su questi due domini e delle principali sfide emergenti con l’obiettivo di favorire il trasferimento tecnologico e la diffusione delle buone pratiche oltre le classiche compartimentazioni settoriali.
Riferimenti bibliografici
1. Abubakar, A. M., Elrehail, H., Alatailat, M. A., and Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. Journal of innovation and knowledge, 4(2): 104-114. https://doi.org/10.1016/j.jik.2017.07.003
2. Acharya, B., Behera, A., Behera, S., and Moharana, S. (2024). Recent advances in nanotechnology-based drug delivery systems for the diagnosis and treatment of reproductive disorders. ACS Applied Bio Materials, 7(3): 1336-1361. https://doi.org/10.1021/acsabm.3c01064.
3. Adams, J. (2023). Defending explicability as principle for the ethics of artificial intelligence in medicine. Medicine, Health Care and Philosophy. https://doi.org/10.1007/s11019-023- 10175-7.
4. Adly, A. S., Adly, A. S., and Adly, M. S. (2020). Approaches based on artificial intelligence and the internet of intelligent things to prevent the spread of COVID-19: scoping review. Journal of medical Internet research, 22(8), e19104. https://doi.org/10.2196/19104.
5. Aerts, A., and Bogdan-Martin, D. (2021). Leveraging data and AI to deliver on the promise of digital health. International Journal of Medical Informatics, 150, 104456. https://doi.org/10.1016/j.ijmedinf.2021.104456.
6. Agarwal, V., Mathiyazhagan, K., Malhotra, S., and Saikouk, T. (2022). Analysis of challenges in sustainable human resource management due to disruptions by Industry 4.0: an emerging economy perspective. International Journal of Manpower, 43(2): 513-541.https://doi.org/10.1108/IJM-03-2021-0192.
7. Ahsan, M. A., Ahmad, K., Ahamed, J., Omar, M., and Ahmad, K. A. B. (2023). PAPQ: Predictive analytics of product quality in industry 4.0. Sustainable Operations and Computers, (4): 53-61. https://doi.org/10.1016/j.susoc.2023.02.001.
8. Ahsan, M. M., and Siddique, Z. (2022). Industry 4.0 in Healthcare: A systematic review. International Journal of Information Management Data Insights, 2(1), 100079. https://doi.org/10.1016/j.jjimei.2022.100079.
9. Ahuja, A., Agrawal, S., Acharya, S., Batra, N., and Daiya, V. (2024). Advancements in wearable digital health technology: a review of epilepsy management. Cureus, 16(3). https://doi.org/10.7759/cureus.57037.
10. Ai, Y., Pan, B., Fu, Y., and Wang, S. (2017) Design of a novel robotic system for minimally invasive surgery. Industrial Robot, 44(3): 288–298. https://doi.org/10.1108/IR-07-2016-0181.
11. Aktürk, C. (2021). Artificial intelligence in enterprise resource planning systems: A bibliometric study. Journal of International Logistics and Trade, 19(2): 69-82. https://doi.org/10.24006/jilt.2021.19.2.069.
12. Al-Amin, M., Hossain, T., Islam, J., and Biwas, S. K. (2023). History, features, challenges, and critical success factors of enterprise resource planning (ERP) in the era of industry 4.0. European scientific journal, 19(6), 31. https://doi.org/10.19044/esj.2023.v19n6p31.
13. Alahmari, N., Alswedani, S., Alzahrani, A., Katib, I., Albeshri, A., and Mehmood, R. (2022). Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia. Sustainability, 14(6): 3313. https://doi.org/10.3390/su14063313.
14. Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem A., Alrashed M., Saleh K.B., Badreldin H. A., Al Yami M.S., Al Harbi S., and Albekairy A.M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1): 689. https://doi.org/10.1186/s12909-023-04698-z.
15. Alsheibani, S. A., Cheung, Y., Messom, C., and Alhosni, M. (2020). Winning ai strategy: Sixsteps to create value from artificial intelligence. AMCIS 2020 Proceedings 1. https://aisel.aisnet.org/amcis2020/adv_info_systems_research/adv_info_systems_research/1.
16. Al-Jaroodi, J., Mohamed, N., and Abukhousa, E. (2020). Health 4.0: on the way to realizing the healthcare of the future. Ieee Access, 8, 211189-211210. https://doi.org/10.1109/ACCESS.2020.3038858.
17. Al-Jaroodi, J., Mohamed, N., Kesserwan, N., and Jawhar, I. (2023). Human factors affecting the adoption of healthcare 4.0. In 2023 IEEE International Systems Conference, 1-7. https://doi.org/10.1109/SysCon53073.2023.10131064.
18. Andronie, M., Lazaroiu, G., Iatagan, M., Hurloiu, I., and Dijmarescu, I. (2021). Sustainable cyber-physical production systems in big data-driven smart urban economy: A systematic literature review. Sustainability, 13(2): 751. https://doi.org/10.3390/su13020751.
19. Angioni, M., and Musso, F. (2020). New perspectives from technology adoption in senior cohousing facilities. The TQM Journal, 32(4): 761-777. https://doi.org/10.1108/TQM-10-2019-0250.
20. Ashima, R., Haleem, A., Bahl, S., Javaid, M., Mahla, S. K., and Singh, S. (2021). Automation and manufacturing of smart materials in Additive Manufacturing technologies using Internet of Things towards the adoption of Industry 4.0. Materials Today: Proceedings, 45, 5081-5088. https://doi.org/10.1016/j.matpr.2021.01.583.
21. Babic, B., Gerke, S., Evgeniou, T., and Cohen, I. G. (2021). Direct-to-consumer medical machine learning and artificial intelligence applications. Nature Machine Intelligence, 3(4):283-287. https://doi.org/10.1038/s42256-021-00331-0.
22. Badidi, E. (2022). Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential. Sustainability, 14(13), 7609. https://doi.org/10.3390/su14137609.
23. Bag, S., Pretorius, J. H. C., Gupta, S., and Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420. https://doi.org/10.1016/j.techfore.2020.120420.
24. Bausano, G. (2024). Intelligenza artificiale: è il momento della medicina generale, nell'interesse dei pazienti. Care. Costi dell'Assistenza e Risorse Economiche, (1/2) 7-7. https://www.proquest.com/openview/14b867e87ef6436c739545997d2aa5b7/1?pqorigsite=gscholar&cbl=7024189.
25. Bécue, A., Praça, I., and Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities. Artificial intelligence review, 54(5): 3849-3886. https://doi.org/10.1007/s10462-020-09942-2.
26. Benbya, H., Davenport, T. H., and Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4). https://doi.org/10.2139/ssrn.3741983.
27. Bettiol, M., Di Maria, E., and Micelli, S. (2020). Industry 4.0 and Knowledge Management: An Introduction. In: Bettiol, M., Di Maria, E., and Micelli, S. (eds) Knowledge Management and Industry 4.0. Knowledge Management and Organizational Learning, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-43589-9.
28. Bharadwaj, P., Nicola, L., Breau-Brunel M., Sensini F., Tanova-Yotova N., Atanasov P., Lobig F., and Blankenburg, M. (2024). Unlocking the value: quantifying the return on investment of hospital artificial intelligence. Journal of the American College of Radiology, 21(10), 1677- 1685. https://doi.org/10.1016/j.jacr.2024.02.034.
29. Biondi, G., Cagnoni, S., Capobianco, R., Franzoni, V., Lisi, F. A., Milani, A., and Vallverdú, J. (2023). Ethical design of artificial intelligence-based systems for decision making. Frontiers in Artificial Intelligence, 6, 1250209. https://doi.org/10.3389/frai.2023.1250209.
30. Bosman, L., Hartman, N., and Sutherland, J. (2020). How manufacturing firm characteristics can influence decision making for investing in Industry 4.0 technologies. Journal of manufacturing technology management, 31(5): 1117-1141. https://doi.org/10.1108/JMTM-09-2018-0283.
31. Brown, S. (2021) Machine Learning, explained – MIT Management: Sloan School. https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained.
32. Brownstein, J.S., Rader, B., Astley, C.M., and Tian, H. (2023). Advances in Artificial Intelligence for Infectious-Disease Surveillance. New England Journal of Medicine,388(17):1597–607. https://doi.org/10.1056/NEJMra2119215.
33. Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert systems, 41(7), e13406. https://doi.org/10.1111/exsy.13406.
34. Capasso, M., and Umbrello, S. (2022). Responsible nudging for social good: new healthcare skills for AI-driven digital personal assistants. Medicine, Health Care and Philosophy, 25(1):11-22. https://doi.org/10.1007/s11019-021-10062-z.
35. Carvalho, N. G. P., and Cazarini, E. W. (2020). Industry 4.0-What Is It? In: Hamilton Ortiz J. (eds) Industry 4.0. Current Status and Future Trends. IntechOpen. DOI:10.5772/intechopen.90068.
36. Chattu, V. K. (2021). A review of artificial intelligence, big data, and blockchain technology applications in medicine and global health. Big Data and Cognitive Computing, 5(3), 41. https://doi.org/10.3390/bdcc5030041.
37. Chee, M. L., Ong, M. E. H., Siddiqui, F. J., Zhang, Z., Lim, S. L., Ho, A. F. W., and Liu, N. (2021). Artificial intelligence applications for COVID-19 in intensive care and emergency settings: a systematic review. International journal of environmental research and public health, 18(9), 4749. https://doi.org/10.3390/ijerph18094749
38. Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., and Yin, B. (2017). Smart factory of industry 4.0: Key technologies, application case, and challenges. Ieee Access, 6: 6505–6519. https://doi.org/10.1109/ACCESS.2017.2783682.
39. Coombs, C. (2020). Will COVID-19 be the tipping point for the intelligent automation of work? A review of the debate and implications for research. International journal of information management, 55, 102182. https://doi.org/10.1016/j.ijinfomgt.2020.102182.
40. Da Costa, M. B., Dos Santos, L. M., Schaefer, J. L., Baierle, I. C. and Nara, E. O. (2019) Industry 4.0 technologies basic network identification. Scientometrics, 121(2): 977–994. https://doi.org/10.1007/s11192-019-03216-7.
41. Darvishi, H., Ciuonzo, D., Eide, E. R., and Rossi, P. S. (2020). Sensor-fault detection, isolation and accommodation for digital twins via modular data-driven architecture. IEEE Sensors Journal, 21(4): 4827–4838. https://doi.org/10.1109/JSEN.2020.3029459
42. Devezas, T., and Sarygulov, A. (2017). Industry 4.0. Basel: Springer. https://doi.org/10.1007/978-3-319-49604-7.
43. Dhanpat, N., Buthelezi, Z. P., Joe, M. R., Maphela, T. V., and Shongwe, N. (2020). Industry 4.0: The role of human resource professionals. SA Journal of Human Resource Management, 18(1): 1-11. https://doi.org/10.4102/sajhrm.v18i0.1302.
44. Di Cataldo, L. (2024). Le relazioni industriali nel secolo della transizione. In: Di Cataldo, L., Occhipinti, M., and Pantaleo V. (eds) Trasformazioni e intersezioni nella società contemporanea: Persone, Istituzioni, Ambiente e Tecnologia. Vol. 2 (pp. 359-387). IPS Edizioni.
45. Di Cataldo L., and Dorigatti L. (2025). Intelligenza artificiale, condizioni di lavoro e relazioni industriali nei servizi labour intensive. Il caso dei contact center. Quaderni di Rassegna Sindacale, 2 (in corso di pubblicazione).
46. Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, 20(1): 1-3. https://doi.org/10.1007/s10676-018-9450-z.
47. Doellgast, V., O’Bradey, S., and Kim, J. (2023). AI in contact centers. USA: Cornell University, ILR School.
48. Dohale, V., Akarte, M., Gunasekaran, A., and Verma, P. (2022). Exploring the role of artificial intelligence in building production resilience: learnings from the COVID-19 pandemic. International Journal of Production Research, 1-17. https://doi.org/10.1080/00207543.2022.2127961.
49. Dwivedi, Y. K., Shareef, M. A., Mukerji, B., Rana, N. P., and Kapoor, K. K. (2018). Involvement in emergency supply chain for disaster management: A cognitive dissonance perspective. International Journal of Production Research, 56(21): 6758-6773. https://doi.org/10.1080/00207543.2017.1378958.
50. Ekins, S. (2016). The next era: deep learning in pharmaceutical research. Pharmaceutical research, 33(11): 2594-2603. https://doi.org/10.1007/s11095-016-2029-7.
51. Elleuch, M. A., Hassena, A. B., Abdelhedi, M., and Pinto, F. S. (2021). Real-time prediction of COVID-19 patients health situations using Artificial Neural Networks and Fuzzy Interval Mathematical modeling. Applied soft computing, 110, 107643. https://doi.org/10.1016/j.asoc.2021.107643.
52. Elzen, B. Geels F.W., Green K. (Eds.). (2004). System Innovation and the Transition to Sustainability: Theory, Evidence and Policy. Edward Elgar, Cheltenham. https://doi.org/10.4337/9781845423421.
53. Esteva A., Kuprel B., Novoa R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639): 115–118. https://doi.org/10.1038/nature21056.
54. Etzioni, A., and Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21(4): 403-418. https://doi.org/10.1007/s10892-017-9252-2.
55. Eurofound (2021), The digital age: Implications of automation, digitisation and platforms for work and employment, Challenges and prospects in the EU series, Publications Office of the European Union, Luxembourg. (ISBN: 978-92-897-2213-1). https://doi.org/10.2806/288.
56. Feng, Q., Tang, W., Zhang, Z., Wei, Y., Ren, L., Chang, W., Zhu D., Liang F., He G., and Xu, J. (2022). Robotic versus laparoscopic abdominoperineal resections for low rectal cancer: A single-center randomized controlled trial. Journal of surgical oncology, 126(8): 1481-1493. https://doi.org/10.1002/jso.27076
57. Floridi, L. (2018). Soft Ethics and the Governance of the Digital. Philosophy and Technology, 31, (1): 1-8. https://doi.org/10.1007/s13347-018-0303-9.
58. Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. Oxford University Press.
https://doi.org/10.1093/oso/9780198883098.001.0001
59. Floridi, L., and Cabitza, F. (2021). Intelligenza artificiale: l'uso delle nuove macchine. Bompiani, Milano. (ISBN-13: 978-8830109384)
60. Floridi L., and Cowls J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1), https://doi.org/10.1162/99608f92.8cd550d1.
61. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V. Luetge C., Madelin R., Pagallo U., Rossi F, Schafer B., Valcke P., and Vayena E. J. M. (2018), AI4People —An Ethical Framework for a Good IA Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines 28(4): 689-707. https://doi.org/10.1007/s11023-018-9482-5.
62. Flynn, R. (2002). Clinical governance and governmentality. Health, risk and society, 4(2):155-173. https://doi.org/10.1080/13698570220137042.
63. Fondazione Umberto Veronesi (2024) La ricerca scientifica nell’era dell’intelligenza artificiale. Documenti di Etica e Bioetica, Comitato Etico Fondazione Umberto Veronesi.
64. Fong, P. Y., Chan, Y. M., and Tang, J. Z. E. (2021). Otogenic Lemierre’s Syndrome With Bilateral Metastatic Pneumonia: Report of an Unusual Case in a Male. Archives of Otorhinolaryngology-Head and Neck Surgery, 6(1): 5. https://doi.org/10.24983/scitemed.aohns.2022.00158.
65. Foruzandeh, E., Jalali, S. M., and Taherikia, F. (2025). Designing an Artificial Intelligence- Based Customer Relationship Management Model to Achieve Competitive Advantage in the Food Industry. Business, Marketing, and Finance Open, 2(3):25-33. https://doi.org/10.61838/bmfopen.2.3.3.
66. Freeman, C., and Louçã F. (2001). As Time Goes By: From the Industrial Revolutions to the Information Revolution. Oxford University Press. https://doi.org/10.1093/0199251053.001.0001
67. Fry, E. A., and Lenert, L. A. (2005) MASCAL: RFID tracking of patients, staff and equipment to enhance hospital response to mass casualty events. AMIA Annual Symposium Proceedings (vol. 2005.). https://pubmed.ncbi.nlm.nih.gov/16779042/.
68. Geels F. W., (2004). From sectoral systems of innovation to socio-technical systems: insights about dynamics and change from sociology and institutional theory. Research Policy, 33(6-7):897-920. https://doi.org/10.1016/j.respol.2004.01.015.
69. Ghayvat, H., Awais, M., Gope, P., Pandya, S., and Majumdar, S. (2021). Recognizing suspect and predicting the spread of contagion based on mobile phone location data (counteract): a system of identifying covid-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence. Sustainable Cities and Society, 69, 102798. https://doi.org/10.1016/j.scs.2021.102798.
70. Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press. (ISBN-13: 978-0262035613).
71. Grandinetti, R. (2016). Absorptive capacity and knowledge management in small and medium enterprises. Knowledge Management Research and Practice, 14(2):159-168. https://doi.org/10.1057/kmrp.2016.2.
72. Gunasekeran, D. V., Tseng, R. M. W. W., Tham, Y. C., and Wong, T. Y. (2021). Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ digital medicine, 4(1):1-6.
https://doi.org/10.1038/s41746-021-00412-9.
73. Hadley, T. D., Pettit, R. W., Malik, T., Khoei, A. A., and Salihu, H. M. (2020). Artificial intelligence in global health—a framework and strategy for adoption and sustainability. International Journal of Maternal and Child Health and AIDS, 9(1):121. https://doi.org/10.21106/ijma.296.
74. Haenlein, M., Kaplan, A., Tan, C. W., and Zhang, P. (2019) Artificial intelligence (AI) and management analytics. Journal of Management Analytics, 6(4):341–343. https://doi.org/10.1080/23270012.2019.1699876.
75. Haleem, A., and Javaid, M. (2020) Medical 4.0 and its role in healthcare during COVID-19 pandemic: A review. Journal of Industrial Integration and Management, 5(04):531-545. https://doi.org/10.1142/S2424862220300045.
76. Hammock, M. L., Chortos, A., Tee, B. C., Tok, J. B., and Bao, Z. (2013). 25th anniversary article: The evolution of electronic skin (e-skin): A brief history, design considerations, and recent progress. Adv Mater, 25(42):5997–6038. https://doi.org/10.1002/adma.201302240.
77. Harding, J. A., Shahbaz, M., and Kusiak, A. (2006). Data mining in manufacturing: a review. Journal of Manufacturing Science and Engineering, 128(4):969-976. https://doi.org/10.1115/1.2194554.
78. Hassanpour, S., and Langlotz, C. P. (2016). Information extraction from multi-institutional radiology reports. Artificial intelligence in medicine, (66):29-39.
https://doi.org/10.1016/j.artmed.2015.09.007.
79. Haug, C. J., and Drazen, J. M. (2023). Artificial Intelligence and Machine Learning in Clinical Medicine, New England Journal of Medicine [Internet]; 388(13):1201–8. Available from: https://doi.org/10.1056/NEJMra2302038.
80. Hofmann, E., Sternberg, H., Chen, H., Pflaum, A., and Prockl, G. (2019). Supply chain management and Industry 4.0: conducting research in the digital age. International Journal of Physical Distribution and Logistics Management, 49(10), 945-955. https://doi.org/10.1108/IJPDLM-11-2019-399.
81. Högberg, C., Larsson, S., and Lång, K. (2024). Engaging with artificial intelligence in mammography screening: Swedish breast radiologists’ views on trust, information and expertise. Digital Health, 10. https://doi.org/10.1177/20552076241287958.
82. Hossain, M. S., Muhammad, G., and Alamri, A. (2019). Smart healthcare monitoring: A voice pathology detection paradigm for smart cities. Multimedia Systems, 25, 565–575. https://doi.org/10.1007/s00530-017-0561-x.
83. Huang, C., Zhang, Z., Mao, B., and Yao, X. (2022). An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799-819.
https://doi.org/10.1109/TAI.2022.3194503.
84. Hughes, L., Dwivedi, Y. K., Rana, N. P., Williams, M. D. and Raghavan, V. (2020) Perspectives on the future of manufacturing within the industry 4.0 era. Production Planning and Control, 1–21. https://doi.org/10.1080/09537287.2020.1810762.
85. Iftikhar, M., Saqib, M., Zareen, M., and Mumtaz, H. (2024). Artificial intelligence:
revolutionizing robotic surgery. Annals of Medicine and Surgery, 86(9), 5401-5409.
https://doi.org/10.1097/MS9.0000000000002426.
86. Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., and Kuusk, A. (2023). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216, 119456. https://doi.org/10.1016/j.eswa.2022.119456.
87. Jan, Z., and Verma, B. (2020). Multicluster class-balanced ensemble. IEEE Transactions on Neural Networks and Learning Systems, 32(3), 1014-1025.
https://doi.org/10.1109/TNNLS.2020.2979839
88. Javaid, M., Haleem, A., Singh, R. P., and Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111. https://doi.org/10.1142/S2424862221300040
89. Jawad, Z. N., and Balázs, V. (2024). Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review. Beni-Suef University Journal of Basic and Applied Sciences, 13(1), 4. https://doi.org/10.1186/s43088-023-00460-y
90. Jin, H., Qi, C., and Chen, Z. (2024). Affective computing for healthcare: Recent trends, applications, challenges, and beyond. Emotional Intelligence, 3. https://doi.org/10.1007/978-981-96-5084-2_1
91. Jobin, A., Ienca, M., and Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
92. Jung, Y., Hur, C., and Kim, M. (2018). Sustainable situation-aware recommendation services with collective intelligence. Sustainability, 10(5), 1632.
https://doi.org/10.3390/su10051632.
93. Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., Kim, B. H., and Noh, S. D. (2016). Smart manufacturing: Past research, present findings, and future directions. International journal of precision engineering and manufacturing-green technology, 3(1), 111-128. https://doi.org/10.1007/s40684-016-0015-5.
94. Kankanhalli, A., Charalabidis, Y., and Mellouli, S. (2019). IoT and AI for smart government: A research agenda. Government Information Quarterly, 36(2), 304-309. https://doi.org/10.1016/j.giq.2019.02.003.
95. Kellogg, K. C., Valentine, M. A., and Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of management annals, 14(1), 366-410.
https://doi.org/10.5465/annals.2018.0174.
96. Khan, M., Mehran, M. T., Haq, Z. U., Ullah, Z., Naqvi, S. R., Ihsan, M., and Abbass, H. (2021). Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert systems with applications, 185. https://doi.org/10.1016/j.eswa.2021.115695.
97. Kjellström, B., Igel, D., Abraham, J., Bennett, T., and Bourge, R. (2005). Trans-telephonic monitoring of continuous haemodynamic measurements in heart failure patients. Journal of telemedicine and telecare, 11(5), 240-244. https://doi.org/10.1258/1357633054471795
98. Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., and Tzovaras, D. (2021). Machine learning and deep learning in smart manufacturing: The smart grid paradigm. Computer Science Review, 40. https://doi.org/10.1016/j.cosrev.2020.100341
99. Kovács, G., Kopácsi, S., Haidegger, G., and Michelini, R. (2006). Ambient intelligence in product life-cycle management. Engineering Applications of Artificial Intelligence, 19(8), 953-965. https://doi.org/10.1016/j.engappai.2006.01.017.
100. KPMG, (2022). Healthcare for all- the greatest gift. https://home.kpmg/xx/en/home/insights/2021/05/healthcare-for-all-the-greatestgift.html
101. Kumar, S., Raut, R. D., and Narkhede, B. E. (2020). A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers. International Journal of Healthcare Management, 13(4), 337-345. https://doi.org/10.1080/20479700.2020.1810453.
102. Kunduru, A. R. (2023). Effective usage of artificial intelligence in enterprise resource planning applications. International Journal of Computer Trends and Technology, 71(4), 73-80. https://doi.org/10.14445/22312803/IJCTT-V71I4P109
103. Kuo, C. L. (2020). Dangers of a false sense of security in a huge mastoid cholesteatoma with skull base erosion and cerebrospinal fluid leakage. Archives of Otorhinolaryngology-Head and Neck Surgery (AOHNS), 4 (2), 5.
https://doi.org/10.24983/scitemed.aohns.2020.00134
104. Kuo, C. L. (2023). Revolutionizing healthcare paradigms: The integral role of artificial intelligence in advancing diagnostic and treatment modalities. International Microsurgery Journal (IMJ);7(1):4. https://doi.org/10.24983/scitemed.imj.2023.00177
105. Kuo, C. L., Chang, W. P., Chang, N. H. Y., Shiao, A. S., and Lien, C. F. (2017). Increased Risk of Depression in Patients with Cholesteatoma. Archives of Otorhinolaryngology-Head and Neck Surgery; 1(3):1. https://doi.org/10.24983/scitemed.aohns.2017.00038
106. Kuo, C. L., and Lien, C. F. (2022). Safe tympanic retraction may be unsafe: A false sense of security in a patient with cholesteatoma. Archives of Otorhinolaryngology-Head and Neck Surgery; 6(2):3. https://doi.org/10.24983/scitemed.aohns.2022.00168
107. Kuo, Y. H., and Kusiak, A. (2019). From data to big data in production research: the past and future trends. International Journal of Production Research, 57(15-16): 4828-4853. https://doi.org/10.1080/00207543.2018.1443230
108. Kusiak, A. (1990). Intelligent manufacturing systems. Englewood Cliffs, NJ: Prentice Hall. (ISBN: 978013468345)
109. Kusiak, A. (2017). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517. https://doi.org/10.1080/00207543.2017.1351644
110. Kusiak, A. (2019). Intelligent manufacturing: bridging two centuries. Journal of intelligent manufacturing, 30(1), 1-2. https://doi.org/10.1007/s10845-018-1455-2.
111. Lalmuanawma, S., Hussain, J., and Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons and Fractals, 139, 110059. https://doi.org/10.1016/j.chaos.2020.110059
112. Larsson, S., and Heintz, F. (2020). Transparency in artificial intelligence. Internet Policy Review. 9. https://doi.org/10.14763/2020.2.1469.
113. Larsson, I., Svedberg, P., Nygren, J. M., and Petersson, L. (2025) Healthcare leaders’ perceptions of the contribution of artificial intelligence to person-centred care: An interview study. Scandinavian Journal of Public Health, 53(1_suppl):72-80.
https://doi.org/10.1177/14034948241307112.
114. LeBlanc, K., Dickens, E., Gonzalez, A., Gamagami, R., Pierce, R., Balentine, C., and Voeller, G. (2020). Prospective, multicenter, pairwise analysis of robotic-assisted inguinal hernia repair with open and laparoscopic inguinal hernia repair: Early results from the prospective hernia study. Hernia 24(5): 1069–1081. https://doi.org/10.1007/s10029-020-02224-4
115. Ledro, C., Nosella, A., and Vinelli, A. (2022). Artificial intelligence in customer
relationship management: literature review and future research directions. Journal of Business and Industrial Marketing, 37(13), 48-63. https://doi.org/10.1108/JBIM-07-2021-0332
116. Lee, J., Davari, H., Singh, J., and Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing letters, 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002
117. Lee, C., and Lim, C. (2021). From technological development to social advance: A review of Industry 4.0 through machine learning. Technological Forecasting and Social Change, 167, 120653. https://doi.org/10.1016/j.techfore.2021.120653
118. Lehne, M., Sass, J., Essenwanger, A., Schepers, J., and Thun, S. (2019). Why digital medicine depends on interoperability. npj Digital Medicine, 2(1), 79.
https://doi.org/10.1038/s41746-019-0158-1
119. Leimanis, A., and Palkova, K. (2021). Ethical guidelines for artificial intelligence in healthcare from the sustainable development perspective. European Journal of Sustainable Development, 10(1): 90-90. https://doi.org/10.14207/ejsd.2021.v10n1p90
120. Lerch, C. M., Heimberger, H., Jäger, A., Horvat, D., and Schultmann, F. (2022). AIreadiness and production resilience: empirical evidence from German manufacturing in times of the Covid-19 pandemic. International Journal of Production Research, 62(15): 1-22. https://doi.org/10.1080/00207543.2022.2141906
121. Li, S. (2024). Optimization of human resources in automated factories based on artificial intelligence in the context of Industry 4.0. The International Journal of Advanced Manufacturing Technology, 1-12. https://doi.org/10.1007/s00170-024-14241-z
122. Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B. and Yang, C. W. (2017) Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology and Electronic Engineering, 18(1): 86–96. https://doi.org/10.1631/FITEE.1601885
123. Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., and Zhou, B. (2023). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9): 1-46.
https://doi.org/10.1145/3555803
124. Liu, Y., Zhang, Y., Ren, S., Yang, M., Wang, Y., and Huisingh, D. (2020). How can smart technologies contribute to sustainable product lifecycle management?. Journal of Cleaner Production, 249, 119423. https://doi.org/10.1016/j.jclepro.2019.119423
125. Liu, Z., Yang, G., and Zhang, Y. (2023). Carbon footprint assessment in manufacturing Industry 4.0 using machine learning with intelligent Internet of things. The International Journal of Advanced Manufacturing Technology, 1-8. https://doi.org/10.1007/s00170-023-12183-6
126. Loorbach, D., Frantzeskaki, N., and Huffenreuter, R. L. (2015). Transition Management. Taking Stock from Governance Experimentation. In Large Systems Change: An Emerging Field of Transformation and Transitions (pp. 48-66). Routledge. https://doi.org/10.4324/9781003579380.
127. Lu, Y., Morris, K. C., and Frechette, S. (2016). Current standards landscape for smart manufacturing systems. National Institute of Standards and Technology, 8107(3): 1-39. https://doi.org/10.6028/NIST.IR.8107.
128. Mah, P. M., Skalna, I., and Muzam, J. (2022). Natural language processing and artificial intelligence for enterprise management in the era of industry 4.0. Applied Sciences, 12(18), 9207. https://doi.org/10.3390/app12189207
129. Manesh, M. F., Pellegrini, M. M., Marzi, G., and Dabic, M. (2020). Knowledge management in the fourth industrial revolution: Mapping the literature and scoping future avenues. IEEE Transactions on Engineering Management, 68(1): 289-300. https://doi.org/10.1109/TEM.2019.2963489
130. Macfarlane, A. J. R. (2019). What is clinical governance?. BJA education, 19(6): 174-175. https://doi.org/10.1016/j.bjae.2019.02.003
131. Mazurek, G., and Małagocka, K. (2019). Perception of privacy and data protection in the context of the development of artificial intelligence. Journal of Management Analytics, 6(4):344–364. https://doi.org/10.1080/23270012.2019.1671243
132. McGreevey, J. D., Hanson, C. W., and Koppel, R. (2020). Clinical, legal, and ethical aspects of artificial intelligence–assisted conversational agents in health care. Jama, 324(6): 552-553. https://doi.org/10.1001/jama.2020.2724
133. Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., ... and Meinert, E. (2020). The effectiveness of artificial intelligence conversational agents in health care: systematic review. Journal of medical Internet research, 22(10). https://doi.org/10.2196/20346
134. Mishra, S., Thakkar, H., Mallick, P. K., Tiwari, P., and Alamri, A. (2021). A Sustainable IoHT based Computationally Intelligent Healthcare Monitoring System for Lung Cancer Risk Detection. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2021.103079
135. Mittal, S., Khan, M. A., Romero, D., and Wuest, T. (2019). Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5): 1342-1361. https://doi.org/10.1177/0954405417736547.
136. Mithas, S., Chen, Z. L., Saldanha, T. J., and De Oliveira Silveira, A. (2022). How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?. Production and Operations Management, 31(12): 4475-4487. https://doi.org/10.1111/poms.13864.
137. Moglia, A., Georgiou, K., Georgiou, E., Satava, R. M., and Cuschieri, A. (2021). A systematic review on artificial intelligence in robot-assisted surgery. International Journal of Surgery, 95. https://doi.org/10.1016/j.ijsu.2021.106151.
138. Moreira, P. A., Fernandes, R. M., Avila, L. V., Bastos, L. D. S. L., and Martins, V. W. B. (2023). Artificial intelligence and industry 4.0? Validation of challenges considering the context of an emerging economy country using Cronbach’s Alpha and the Lawshe method. Eng, 4(3), 2336-2351. https://doi.org/10.3390/eng4030133
139. Mrówczyńska, M., Sztubecka, M., Skiba, M., Bazan-Krzywoszańska, A., and Bejga, P. (2019). The use of artificial intelligence as a tool supporting sustainable development local policy. Sustainability, 11(15). https://doi.org/10.3390/su11154199
140. Mun, J., Housel, T., Jones, R., Carlton, B., and Skots, V. (2020). Acquiring artificial intelligence systems: Development challenges, implementation risks, and cost/benefits opportunities. Naval Engineers Journal, 132(2), 79-94.
141. Murero, M., and Punziano, G. (2025). Intelligenza Artificiale in Medicina: Literacy, Percezioni ed Esperienze tra Futuri Plausibili e Nuove Disuguaglianze. Rivista trimestrale di scienza dell'amministrazione, 1, 1-44. https://dx.doi.org/10.32049/RTSA.2025.1.04.
142. Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press. (ISBN: 9780262018029).
143. Nakagawa, E. Y., Antonino, P. O., Schnicke, F., Kuhn, T., and Liggesmeyer, P. (2021). Continuous systems and software engineering for industry 4.0: A disruptive view. Information and software technology, 135. https://doi.org/10.1016/j.infsof.2021.106562
144. Naseem, M., Akhund, R., Arshad, H., and Ibrahim, M. T. (2020). Exploring the potential of artificial intelligence and machine learning to combat COVID-19 and existing opportunities for LMIC: a scoping review. Journal of Primary Care and Community Health, 11. https://doi.org/10.1177/2150132720963634
145. Nasseef, O. A., Baabdullah, A. M., Alalwan, A. A., Lal, B., and Dwivedi, Y. K. (2021). Artificial intelligence-based public healthcare systems: G2G knowledge-based exchange to enhance the decision-making process. Government Information Quarterly. https://doi.org/10.1016/j.giq.2021.101618.
146. Nesari, M., Naghizadeh, M., Ghazinoori, S., and Manteghi, M. (2022). The evolution of socio-technical transition studies: a scientometric analysis. Technology in Society, 68. https://doi.org/10.1016/j.techsoc.2021.101834.
147. Newbert, S. L., (2007). Empirical research on the resource-based view of the firm: an assessment and suggestions for future research. Strategic Management Journal, (28): 121–146. https://doi.org/10.1002/smj.573.
148. Nuttah, M. M., Roma, P., Nigro, G. L., and Perrone, G. (2023). Understanding blockchain applications in Industry 4.0: From information technology to manufacturing and operations management. Journal of Industrial Information Integration, 33. https://doi.org/10.1016/j.jii.2023.100456.
149. Oikonomou, E. K., and Khera, R. (2025). Expanding artificial intelligence to understudied populations: congenital heart disease as the next frontier, European Heart Journal, 46(9): 869–871. https://doi.org/10.1093/eurheartj/ehae737.
150. ONU (2022). Sustainable Development Goals. Goal 3: Ensure healthy lives and promotes well-being for all at all ages. Available at: https://www.un.org/sustainabledevelopment/health/
151. Oztemel, E., and Gursev, S. (2020). Literature review of industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1): 127–182.
https://doi.org/10.1007/s10845-018-1433-8.
152. Pandey, S., Gupta, S., and Chhajed, S. (2021). ROI of AI: Effectiveness and
measurement. International Journal Of Engineering Research and Technology, 10.
https://doi.org/10.2139/ssrn.3858398.
153. Park, K. (2013). Facing the truth about nanotechnology in drug delivery. ACS Nano,7(9): 7442–7447. https://doi.org/10.1021/nn404501g.
154. Parviainen, J., and Rantala, J. (2021). Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care. Medicine, Health Care and Philosophy, 1-11. https://doi.org/10.1007/s11019-021-10049-w.
155. Pascu-Gabara, E. I., and Cepoi, A. (2021). Innovative solutions to overcome the health services crisis within the Covid-19 era. IBIMA Business Review.
https://doi.org/10.5171/2021.907184.
156. Peng, Y., Liu, E., Peng, S., Chen, Q., Li, D., and Lian, D. (2022). Using artificial intelligence technology to fight COVID-19: a review. Artificial intelligence review, 55(6), 4941-4977. https://doi.org/10.1007/s10462-021-10106-z.
157. Pereira, A. G., Lima T.M. and Charrua-Santos F. (2020). Industry 4.0 and society 5.0: Opportunities and threats. International Journal of Recent Technology and Engineering, 8(5), 3305–3308. https://doi.org/10.35940/ijrte.D8764.018520
158. Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., and Barata, J. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE access, 8, 220121-220139. https://doi.org/10.1109/ACCESS.2020.3042874.
159. Pokhrel, S. R., Pan, L., Kumar, N., Doss, R., and Vu, H. L. (2021). Multipath TCP meets transfer learning: A novel edge-based learning for industrial IoT. IEEE Internet of Things Journal, 8(13), 10299-10307. https://doi.org/10.1109/JIOT.2021.3056466.
160. Pollack, M. E. (2005). Intelligent technology for an aging population: The use of ai to assist elders with cognitive impairment. AI Magazine, 26(2),9.
https://doi.org/10.1609/aimag.v26i2.1810.
161. Popkova, E. G., and Sergi, B. S. (2022). Digital public health: Automation based on new datasets and the Internet of Things. Socio-Economic Planning Sciences, 80. https://doi.org/10.1016/j.seps.2021.101039.
162. Pourhosseini, S. S., Ardalan, A., and Mehrolhassani, M. H. (2015). Key aspects of providing healthcare services in disaster response stage. Iranian journal of public health, 44(1), 111. https://pubmed.ncbi.nlm.nih.gov/26060782/.
163. Puhovichova, D., and Jankelova, N. (2020). Changes of human resource management in the context of impact of the fourth industrial revolution. Industry 4.0, 5(3): 138-141. https://stumejournals.com/journals/i4/2020/3/138.
164. Radanliev, P., De Roure, D., Maple, C., and Ani, U. (2022a). Super-forecasting the ‘technological singularity’risks from artificial intelligence. Evolving Systems, 13(5), 747-757. https://doi.org/10.1007/s12530-022-09431-7.
165. Radanliev, P., De Roure, D., Maple, C., and Santos, O. (2022b). Forecasts on future evolution of artificial intelligence and intelligent systems. Ieee Access, 10, 45280-45288. https://doi.org/10.1109/ACCESS.2022.3169580.
166. Rahman, M. A., Hossain, M. S., Showail, A. J., Alrajeh, N. A., and Alhamid, M. F. (2021a). A Secure, Private, and Explainable IoHT Framework to Support Sustainable Health Monitoring in a Smart City. Sustainable Cities and Society.
https://doi.org/10.1016/j.scs.2021.103083.
167. Rahman, M. M., Khatun, F., Uzzaman, A., Sami, S. I., Bhuiyan, M. A. A., and Kiong, T. S. (2021b). A comprehensive study of artificial intelligence and machine learning approaches in confronting the coronavirus (COVID-19) pandemic. International Journal of Health Services, 51(4), 446-461. https://doi.org/10.1177/00207314211017469.
168. Rahwan, I. (2018). Society in the Loop: Programming the Algorithmic Social Contract. Ethics and Information Technology. 20(1): 5-14. https://doi.org/10.1007/s10676-017-9430-8.
169. Rajpurkar, P., and Lungren, M. P. (2023). The Current and Future State of AI
Interpretation of Medical Images. New England Journal of Medicine,388(21): 1981–90. Available from: https://doi.org/10.1056/NEJMra2301725
170. Ramesh, A., and Chawla, V. (2022). Chatbots in marketing: A literature review using morphological and co-occurrence analyses. Journal of Interactive Marketing, 57(3): 472-496. https://doi.org/10.1177/10949968221095549.
171. Rana, G., and Sharma, R. (2019). Emerging human resource management practices in Industry 4.0. Strategic HR Review, 18(4): 176-181. https://doi.org/10.1108/SHR-01-2019-0003
172. Rauch, E., Linder, C., and Dallasega, P. (2020). Anthropocentric perspective of production before and within Industry 4.0. Computers and Industrial Engineering, 139, 105644. https://doi.org/10.1016/j.cie.2019.01.018
173. Richie, C. (2022). Environmentally sustainable development and use of artificial intelligence in health care. Bioethics, 36(5): 547-555. https://doi.org/10.1111/bioe.13018.
174. Rikalovic, A., Suzic, N., Bajic, B. and Piuri, V. (2022), “Industry 4.0 Implementation Challenges and Opportunities: A Technological Perspective”, IEEE System Journal, 16 (2): 2797–2810. https://doi.org/10.1109/JSYST.2021.3101673
175. Rojek, I., Macko, M., Mikołajewski, D., Sága, M., and Burczyński, T. (2021). Modern methods in the field of machine modelling and simulation as a research and practical issue related to Industry 4.0. Bulletin of the Polish Academy of Sciences. Technical Sciences, 69(2). https://doi.org/10.24425/bpasts.2021.136717
176. Rotmans, J., Kemp, R., and Van Asselt, M. (2001). More evolution than revolution: transition management in public policy, Foresight, 3 (1): 15-31.
https://doi.org/10.24425/bpasts.2021.136717
177. Rotmans, J., and Loorbach, D. (2001). Transition management. A new steering model. ArenA Lucht, 7(6), 5-8.
178. Rotmans, J., Van Asselt, M., Anastasi, C., Greeuw, S., Mellors, J., Peters, S., Rotham, D., and Rijkens, N. (2000). Visions for a sustainable Europe, Futures, 32(9-10): 809-831; https://doi.org/10.1016/S0016-3287(00)00033-1
179. Saba, L., Biswas, M., Kuppili, V., Godia, E. C., Suri, H. S., Edla, D. R., and Protogerou, A. (2019). The present and future of deep learning in radiology. European Journal of Radiology, 114, 14–24. https://doi.org/10.1016/j.ejrad.2019.02.038.
180. Samarasinghe, K. R., and Medis, A. (2020). Artificial intelligence based strategic human resource management (AISHRM) for industry 4.0. Global journal of management and business research, 20(2): 7-13. https://doi.org/10.34257/GJMBRGVOl20IS2PG7
181. Samuel, A. L. (1959). Machine learning. The Technology Review, 62(1), 42-45.
182. Samuels, A. (2025). Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: a systematic literature review. Frontiers in artificial intelligence, 7. https://doi.org/10.3389/frai.2024.1477044.
183. Sarbadhikari, S. N., and Pradhan, K. B. (2020). The need for developing Technology-Enabled, safe, and ethical workforce for healthcare delivery. Safety and Health at Work, 11(4): 533-536. https://doi.org/10.1016/j.shaw.2020.08.003.
184. Satpathy, S., Mangla, M., Sharma, N., Deshmukh, H., and Mohanty, S. (2021). Predicting mortality rate and associated risks in COVID-19 patients. Spatial Information Research, 29(4): 455-464. https://doi.org/10.1007/s41324-021-00379-5.
185. Saygin, A. P., Cicekli, I., and Akman, V. (2000). Turing test: 50 years later. Minds and Machines, 10(4): 463–518. https://doi.org/10.1023/A:1011288000451.
186. Schwab, K. (2016). The Fourth Industrial Revolution. Random House USA Inc. (ISBN-13: 978-0241300756).
187. Scott, B. K., Miller, G. T., Fonda, S. J., Yeaw, R. E., Gaudaen, J. C., Pavliscsak, H. H., Quinn, M. T., and Pamplin, J. C. (2020). Advanced digital health technologies for COVID-19 and future emergencies. Telemedicine and e-Health, 26(10): 1226-1233.
https://doi.org/10.1089/tmj.2020.0140.
188. Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., and Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1): 1-23. https://doi.org/10.1186/s12911-021-01488-9.
189. Sharma, G. D., Yadav, A., and Chopra, R. (2020). Artificial intelligence and effective governance: A review, critique and research agenda. Sustainable Futures, 2. https://doi.org/10.1016/j.sftr.2019.100004.
190. Siau, K., and Wang, W. (2018), Building Trust in Artificial Intelligence, Machine Learning, and Robotics. Cutter Business Technology Journal (31): 47–53.
191. Simon, H. A. (1991). Bounded rationality and organizational learning. Organization science, 2(1): 125-134. https://doi.org/10.1287/orsc.2.1.125
192. Singh, R. K., Kumar, P., and Chand, M. (2021). Evaluation of supply chain coordination index in context to Industry 4.0 environment. Benchmarking: An International Journal, 28(5): 1622-1637. https://doi.org/10.1108/BIJ-07-2018-0204
193. Skilton, M., and Hovsepian, F. (2018). The 4th industrial revolution. Springer Nature. https://doi.org/10.1007/978-3-319-62479-2
194. Sofiyah, F. R., Dilham, A., Hutagalung, A. Q., Yulinda, Y., Lubis, A. S., and Marpaung, J. L. (2024). The chatbot artificial intelligence as the alternative customer services strategic to improve the customer relationship management in real-time responses. International Journal of Economics and Business Research, 27(5): 45-58. https://doi.org/10.1504/IJEBR.2024.139810.
195. Stanisławski, R., and Szymonik, A. (2021). Impact of selected intelligent systems in logistics on the creation of a sustainable market position of manufacturing companies in Poland in the context of Industry 4.0. Sustainability, 13(7). https://doi.org/10.3390/su13073996.
196. Straw, I. (2020). The automation of bias in medical Artificial Intelligence (AI): Decoding the past to create a better future. Artificial intelligence in medicine, 110.
https://doi.org/10.1016/j.artmed.2020.101965.
197. Sucandy I., Rayman S., Lai E.C., Tang, C.-N., Chong, Y., Efanov, M., Fuks, D., Choi, G.-H., Chong, C. C., Chiow, A. K. H., Marino, M., V., Prieto, M., Lee, J.-H., Kingham, T. P., D'Hondt, M., Troisi, R. I., Choi, S. H., Sutcliffe, R. P., Cheung, T.-T., Rotellar, F., Park, O. J., Scatton, O., Han, H.-S., and Pratschke, J., (2022). Robotic versus laparoscopic left and extended left hepatectomy: An international multicenter study propensity score-matched analysis. Annals of Surgical Oncology, 29(13): 8398–8406. https://doi.org/10.1245/s10434-022-12216-6.
198. Sun, T. Q., and Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2): 368-383. https://doi.org/10.1016/j.giq.2018.09.008.
199. Sun, Y., Xu, H., Li, Z., Han, J., Song, W., Wang, J., and Xu, Z. (2016). Robotic versus laparoscopic low anterior resection for rectal cancer: a meta-analysis. World journal of surgical oncology, 14(1): 61. https://doi.org/10.1186/s12957-016-0816-6.
200. Taherdoost, H., and Madanchian, M. (2023). Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation. Computers, 12(4): 72. https://doi.org/10.3390/computers12040072
201. Tan, N. G., Yang, L. W. Y., Tan, M. Z. W., Chng, J., Tan, M. H. T., and Tan, C. (2022). Virtual care to increase military medical centre capacity in the primary health care setting: A prospective self-controlled pilot study of symptoms collection and telemedicine. Journal of telemedicine and telecare, 28(8): 603-612. https://doi.org/10.1177/1357633X20959579.
202. Taranto-Vera, G., Galindo-Villardón, P., Merchán-Sánchez-Jara, J., Salazar-Pozo, J., Moreno-Salazar, A., and Salazar-Villalva, V. (2021). Algorithms and software for data mining and machine learning: A critical comparative view from a systematic review of the literature. The Journal of Supercomputing, 1–33. https://doi.org/10.1007/s11227-021-03708-5.
203. Tucker, G. (2021). Sustainable product lifecycle management, industrial big data, and internet of things sensing networks in cyber-physical system-based smart factories. Journal of Self-Governance and Management Economics, 9(1): 9-19. https://doi.org/10.22381/jsme9120211.
204. Tuffnell, C., Kral, P., Durana, P., and Krulicky, T. (2019) Industry 4.0-based
manufacturing systems: Smart production, sustainable supply chain networks, and realtime process monitoring. Journal of Self-Governance and Management Economics, 7(2): 7– 12. https://doi.org/10.22381/JSME7220191
205. Tung, K. (2019). AI, the internet of legal things, and lawyers. Journal of Management Analytics, 6(4): 390–403. https://doi.org/10.1080/23270012.2019.1671242.
206. Turing, A. M. (1950) Computing Machinery and Intelligence. Mind,(49): 433-460 https://doi.org/10.1093/mind/LIX.236.433.
207. Uhl-Bien, M., Marion, R., and McKelvey, B. (2007). Complexity leadership theory: Shifting leadership from the industrial age to the knowledge era. The leadership quarterly, 18 (4): 298–318. https://doi.org/10.1016/j.leaqua.2007.04.002.
208. Umbrello, S., Capasso, M., Balistreri, M., Pirni, A., and Merenda, F. (2021). Value sensitive design to achieve the UN SDGs with AI: A case of elderly care robots. Minds and Machines, 31(3): 395-419. https://doi.org/10.1007/s11023-021-09561-y
209. Unberath, M., Ghobadi, K., Levin, S., Hinson, J., and Hager, G. D. (2020). Artificial Intelligence-Based Clinical Decision Support for COVID-19–Where Art Thou?. Advanced Intelligent Systems, 2(9). https://doi.org/10.1002/aisy.202000104
210. Vaishya, R., Javaid, M., Khan, I. H., and Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(4): 337-339. https://doi.org/10.1016/j.dsx.2020.04.012.
211. Van Driel, H., and Schot J. W., (2005) Radical innovation as a multilevel process: introducing floating grain elevators in the port of Rotterdam. Technology and Culture, 46(1), 51-76. https://doi.org/10.1353/tech.2005.0011
212. Van Geenhuizen, M., and Soetanto, D. P. (2009). Academic spin-offs at different ages: A case study in search of key obstacles to growth. Technovation, 29(10): 671–681. https://doi.org/10.1016/j.technovation.2009.05.009.
213. Xie, Y., Yin, Y., Xue, W., Shi, H., and Chong, D. (2020). Intelligent supply chain
performance measurement in Industry 4.0. Systems Research and Behavioral Science, 37(4): 711-718. https://doi.org/10.1002/sres.2712.
214. Xu, L. D. (2021). Special issue on system research on artificial intelligence. Systems Research and Behavioral Science, 38(1). https://doi.org/10.1002/sres.2776.
215. Walmsley, J. (2021). Artificial intelligence and the value of transparency. AI and society, 36(2): 585-595. https://doi.org/10.1007/s00146-020-01066-z.
216. Wan, J., Li, X., Dai, H.-N., Kusiak, A., Martínez-García, M., and Li, D. (2020).
Artificialintelligence-driven customized manufacturing factory: Key technologies,
applications, and challenges. Proceedings of the IEEE, 109(4): 377–398. https://doi.org/10.1109/JPROC.2020.3034808.
217. Wang, L., and Alexander, C. A. (2021). COVID-19: a pandemic challenging healthcare system. IISE Transactions on Healthcare Systems Engineering, 11(4): 271-292. https://doi.org/10.1080/24725579.2021.1933269.
218. Wang, L., Liu, Z., Liu, A., and Tao, F. (2021). Artificial intelligence in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 114(3): 771-796. https://doi.org/10.1007/s00170-021-06882-1.
219. Wang, X., Zhang, Z., Yang, L., and Zhao, J. (2021a). Price and capacity decisions in a telemedicine service system under government subsidy policy. International Journal of Production Research, 59(17): 5130-5143.
https://doi.org/10.1080/00207543.2020.1774090
220. Wang, W. T., and Wu, S. Y. (2021b). Knowledge management based on information technology in response to COVID-19 crisis. Knowledge Management Research and Practice, 19(4): 468-474. https://doi.org/10.1080/14778238.2020.1860665.
221. Winfield, A. F., Booth, S., Dennis, L. A., Egawa, T., Hastie, H., Jacobs, N., Muttram, R., Olszewska, J. I., Rajabiyazdi, F., Theodoru, A., Underwood, M. A., Wortham, R. H., and Watson, E. (2021). IEEE P7001: A proposed standard on transparency. Frontiers in Robotics and AI, 8. https://doi.org/10.3389/frobt.2021.665729.
222. Wu, Y., Li, X., Fu, X., Huang, X., Zhang, S., Zhao, N., Ma, X., Saiding, Q., Yang, M., Tao, W., Zhou, X., and Huang, J. (2024). Innovative nanotechnology in drug delivery systems for advanced treatment of posterior segment ocular diseases. Advanced science, 11(32). https://doi.org/10.1002/advs.202403399.
223. Wu, Q., Ren, H., Shi, S., Fang, C., Wan, S., and Li, Q. (2023). Analysis and prediction of industrial energy consumption behavior based on big data and artificial intelligence. Energy Reports, (9): 395-402. https://doi.org/10.1016/j.egyr.2023.01.007.
224. Yigitcanlar, T., and Cugurullo, F. (2020). The sustainability of artificial intelligence: An urbanistic viewpoint from the lens of smart and sustainable cities. Sustainability, 12(20): 8548. https://doi.org/10.3390/su12208548.
225. Zahid, A., Poulsen, J. K., Sharma, R., and Wingreen, S. C. (2021). A systematic review of emerging information technologies for sustainable data-centric health-care. International Journal of Medical Informatics, 149. https://doi.org/10.1016/j.ijmedinf.2021.104420.
226. Zemmar, A., Lozano, A. M., and Nelson, B. J. (2020). The rise of robots in surgical environments during COVID-19. Nature Machine Intelligence, 2(10): 566-572. https://doi.org/10.1038/s42256-020-00238-2.
227. Zhang, L., Luo, Y-L., Tao, F., Li, B.-H., Ren, L., and Zhang, X. (2014). Cloud Manufacturing: A New Manufacturing Paradigm. Enterprise Information Systems, 8(2): 167–187. https://doi.org/10.1080/17517575.2012.683812
228. Zhang, Y., Ma, S., Yang, H., Lv, J., and Liu, Y. (2018). A big data driven analytical
framework for energy-intensive manufacturing industries. Journal of Cleaner
Production, (197): 57-72. https://doi.org/10.1016/j.jclepro.2018.06.170.
229. Zhang, X., Ming, X., and Yin, D. (2020). Application of industrial big data for smart manufacturing in product-service system based on system engineering using fuzzy DEMATEL. Journal of Cleaner Production, 265.
https://doi.org/10.1016/j.jclepro.2020.121863.
230. Zhao, W., Luo, X., and Qiu, T. (2017). Smart Healthcare. Applied Sciences, 7(11), 1176. https://doi.org/10.3390/app7111176.
231. Zhong, R. Y., Xu, C., Chen, C., and G. Q. Huang. (2017). Big Data Analytics for Physical Internet-based Intelligent Manufacturing Shop Floors. International Journal of Production Research, 55(9): 2610–2621. https://doi.org/10.1080/00207543.2015.1086037.
232. Zong, Z., and Guan, Y. (2025). AI-driven intelligent data analytics and predictive analysis in Industry 4.0: Transforming knowledge, innovation, and efficiency. Journal of the Knowledge Economy, 16(1): 864-903. https://doi.org/10.1007/s13132-024-02001-z.
233. Zonta, T., Da Costa, C. A., da Rosa Righi, R., de Lima, M. J., Da Trindade, E. S., and Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers and industrial engineering, 150.
