Analisi critica e multidisciplinare dell’impatto dell’IA su industria e sanità
DOI:
https://doi.org/10.82015/NNR.2025.100101Parole chiave:
Intelligenza Artificiale; Industria 4.0; Sanità Digitale; Diritti Neurali; Etica dell'IA; Sviluppo Sostenibile; Neurotecnologie; Cyber-Utopismo; Equità; Governance.Abstract
La convergenza tra tecnologie digitali avanzate, in particolare l’Intelligenza Artificiale (IA), e i settori industriali e sanitari sta ridefinendo in modo irrevocabile i paradigmi operativi, economici, sociali ed etici della nostra epoca. Questa “trasformazione intelligente”, spesso narrata attraverso un lens ottimistico e cyber-utopistico, promette efficienza, sostenibilit. e personalizzazione senza precedenti. Tuttavia, essa solleva simultaneamente interrogativi profondi sull'equit., la giustizia distributiva, la privacy, l'autonomia umana e la stessa natura della coscienza e dell'intelligenza. Attraverso un'analisi critica e multidisciplinare della letteratura – che spazia dalla filosofia del diritto alle neuroscienze, dall'economia politica all'etica applicata – questo articolo si propone di esplorare in profondit. le implicazioni di questa transizione epocale. Partendo dalle riflessioni critiche (Di Salvo 2024, 2025) sul cyber-utopismo e sui diritti neurali, verranno analizzate le opportunit. di progresso autentico e le sfide sistemiche da affrontare per orientare lo sviluppo tecnologico verso un futuro veramente equo, sostenibile e umano-centrico. L'obiettivo . fornire una panoramica completa e sfaccettata che vada oltre la retorica tecnofila o tecnofoba, offrendo invece una base solida per un dibattito informato e costruttivo.
Riferimenti bibliografici
1. Acemoglu, D., and Restrepo, P. (2020). The wrong kind of AI? Artificial intelligence and the future of labour demand. Cambridge Journal of Regions, Economy and Society, 13(1): 25–35. https://doi.org/10.1093/cjres/rsz022
2. Barocas, S., and Selbst, A. D. (2016). Big data's disparate impact. California Law Review, (104): 671–732. https://doi.org/10.15779/Z38BG31
3. Berkhout, F., and Hertin, J. (2004). De-materialising and re-materialising: digital technologies and the environment. Futures, 36(8): 903–920. https://doi.org/10.1016/j.futures.2004.01.003
4. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512
5. Carr, N. (2014). The glass cage: Automation and us. W. W. Norton e Company.
6. Di Salvo, M. (2024a). Artificial Intelligence and the cyber utopianism of justice. Why AI is not intelligence and man’s struggle to survive himself. Russian Journal of Economics and Law, 18(1): 264–279. https://doi.org/10.21202/2782-2923.2024.1.264-279
7. Di Salvo, M. (2024b). Inteligencias múltiples y especialización: Un retorno a Gardner y la necesidad de repensar la psicología en una transparencia hillmaniana. Revista De Estudios Socioeducativos. ReSed, 1(12). Recuperato a partire da https://revistas.uca.es/index.php/ReSed/article/view/11000
8. Di Salvo, M. (2025a). An Equitable and Sustainable Development model in the era of global crises. Revista De Estudios Socioeducativos. ReSed, 1(13). https://doi.org/10.25267/Rev_estud_socioeducativos.2025.i13.13
9. Di Salvo, M. (2025b). The protection of neural rights in the age of neurotechnologies and AI. the ethical challenge for law and neuroscience. Russian Journal of Economics and Law, 19(1): 202-233. https://doi.org/10.21202/2782-2923.2025.1.202-233
10. Di Salvo, M. (2025c). Neurociencia y educación en la transición de lo analógico a la IA. Revista Internacional de Teoría e Investigación Educativa, 3, e101207. https://doi.org/10.5209/ritie.101207.
11. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
12. European Commission. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union. https://doi.org/10.2777/308407.
13. 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.
14. Flores, M., Glusman, G., Brogaard, K., Price, N. D., and Hood, L.(2013). P4 medicine: how systems medicine will transform the healthcare sector and society. Personalized Medicine, 10(6): 565–576. https://doi.org/10.2217/pme.13.57.
15. 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. (2018). AI4People— An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4): 689–707. https://doi.org/10.1007/s11023-018-9482-5.
16. Frey, C. B., and Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, (114): 254–280. https://doi.org/10.1016/j.techfore.2016.08.019.
17. Friedman, B., Kahn, P. H., Borning, A., and Huldtgren, A. (2013). Value sensitive design and information systems. In Early engagement and new technologies: Opening up the laboratory (pp. 55-95). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7844-3_4.
18. Gardner, H. (2011). Frames of mind: The theory of multiple intelligences. Basic Books.
19. Goodman, B., and Flaxman, S. (2017). European Union regulations on algorithmic decisionmaking and a "right to explanation". AI Magazine, 38(3): 50–57. https://doi.org/10.1609/aimag.v38i3.2741.
20. He, Y., Niu, J., and Gao, K. (2020). A survey on security and privacy in electronic health records. IEEE Access, (8): 183942-183953. https://doi.org/10.1109/ACCESS.2020.3029595.
21. Hermann, M., Pentek, T., and Otto, B. (2016). Design principles for industrie 4.0 scenarios. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 3928-3937). IEEE. https://doi.org/10.1109/HICSS.2016.488.
22. Ienca, M., and Andorno, R. (2017). Towards new human rights in the age of neuroscience and neurotechnology. Life Sciences, Society and Policy, 13(1): 1-27. https://doi.org/10.1186/s40504-017-0050-1.
23. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., and Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4): 230-243. http://dx.doi.org/10.1136/svn-2017-000101.
24. 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
25. Kagermann, H., Wahlster, W., and Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Final report of the Industrie 4.0 Working Group. Acatech. https://doi.org/10.3390/sci4030026
26. Kellmeyer, P. (2018). Artificial intelligence in basic and clinical neuroscience: opportunities and ethical challenges. Neuroethics, 12(3): 257-265. https://doi.org/10.1007/s12152-018-0955-9
27. Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., and Coiera, E. (2018). Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association, 25(9): 1248-1258. https://doi.org/10.1093/jamia/ocy072
28. Lavazza, A. (2018). Freedom of thought and mental integrity: the moral requirements for any neural prosthesis. Frontiers in Neuroscience, (12): 82. https://doi.org/10.3389/fnins.2018.00082
29. Lee, J., Bagheri, B., and Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, (3): 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001
30. Long, D., and Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). https://doi.org/10.1145/3313831.3376727
31. Maddern, S., Rimmer, R., Hanna, G. B., and Slack, M. (2020). The future of robotic surgery: a review of new technologies and their applications. ANZ Journal of Surgery, 90(7-8): 1299-1305. https://doi.org/10.1111/ans.16017
32. Matheson, E., Minto, R., Zampieri, E. G., Faccio, M., and Rosati, G. (2019). Human–robot collaboration in manufacturing applications: A review. Robotics, 8(4). 100. https://doi.org/10.3390/robotics8040100
33. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D., Mostofi, H., Peng, L., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K. C., De Fauw, J., and Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788): 89-94. https://doi.org/10.1038/s41586-019-1799-6.
34. Morozov, E. (2013). To save everything, click here: The folly of technological solutionism. PublicAffairs.
35. Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S. (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464): 447-453. https://doi.org/10.1126/science.aax2342
36. O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
37. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.
38. Price, W. N., Gerke, S., and Cohen, I. G. (2019). Potential liability for physicians using artificial intelligence. JAMA, 322(18): 1765-1766.
https://doi.org/10.1001/jama.2019.15064
39. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., and Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33-44). https://doi.org/10.1145/3351095.3372873
40. Rumsfeld, J. S., Joynt, K. E., and Maddox, T. M. (2016). Big data analytics to improve cardiovascular care: promise and challenges. Nature Reviews Cardiology, 13(6): 350-359. https://doi.org/10.1038/nrcardio.2016.42
41. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., and Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group.
42. Sadeghi, A. R., Wachsmann, C., and Waidner, M. (2015). Security and privacy challenges in industrial internet of things. In 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) (pp. 1-6). https://doi.org/10.1145/2744769.2747942
43. Stock, T., and Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP, (40): 536-541. https://doi.org/10.1016/j.procir.2016.01.129
44. Susser, D., Roessler, B., and Nissenbaum, H. (2019). Technology, autonomy, and manipulation. Internet Policy Review, 8(2). https://doi.org/10.14763/2019.2.1410
45. Topol, E. J. (2019). Deep medicine: how artificial intelligence can make healthcare human again. Basic Books.
46. Wang, S., Wan, J., Li, D., and Zhang, C. (2016). Implementing smart factory of industrie 4.0: an outlook. International Journal of Distributed Sensor Networks, 12(1): 3159805. https://doi.org/10.1155/2016/3159805
47. World Economic Forum. (2020). The future of jobs report 2020. https://www.weforum.org/reports/the-future-of-jobs-report-2020/
48. Xu, L. D. (2012). Information architecture for supply chain quality management. International Journal of Production Research, 50(24): 7373-7387. https://doi.org/10.1080/00207543.2011.648278
49. Xu, X., David, J. M., and Kim, S. H. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9(2): 90-95. https://doi.org/10.5430/ijfr.v9n2p90
50. Yuste, R., Goering, S., Arcas, B. A. Y., Bi, G., Carmena, J. M., Carter, A., Fins, J. J., Friesen, P., Gallant, J., Huggins, J. E., Illes, J., Kellmeyer, P., Klein, E., Marblestone, A., Mitchell, C., Parens, E., Pham, M., Rubel, A., Sadato, N., Specker Sullivan, L., Teicher, M., Wasserman, D., Wexler,
A., Whittaker, M. and Wolpaw, J. (2017). Four ethical priorities for neurotechnologies and AI. Nature News, 551(7679): 159. https://doi.org/10.1038/551159a
51. Zanella, A., Bui, N., Castellani, A., Vangelista, L., and Zorzi, M. (2014). Internet of Things for smart cities. IEEE Internet of Things Journal, 1(1): 22-32. https://doi.org/10.1109/JIOT.2014.2306328
52. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
