Analysis of The Influence of Artificial Intelligence (Ai) Technology on Modern Life
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Abstract
This study examines the impact of artificial intelligence (AI) technology on modern life using a literature review approach. Data were obtained through a search on Google Scholar of relevant literature published between 2020-2025, using descriptive qualitative methods and content analysis techniques. The results show that AI has had a transformative impact on various sectors: the healthcare sector through Electronic Health Record (EHR) systems and improved patient safety; the education sector through personalized learning and virtual classes; as well as the business sector through chatbots, recommendation engines, and e-commerce logistics systems. While AI offers significant benefits in productivity and efficiency, it also poses challenges, including potential unemployment due to automation and data security risks. AI implementation requires careful regulatory considerations, with the industry needing to optimize the use of AI to increase competitiveness while addressing security and workforce impacts.
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References
[1] H. A. Abbass, “Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust,” Cognit Comput, vol. 11, no. 2, pp. 159–171, Jan. 2019, doi: 10.1007/S12559-018-9619-0.
[2] M. Shin, J. Kim, B. van Opheusden, and T. L. Griffiths, “Superhuman artificial intelligence can improve human decision-making by increasing novelty,” Proceedings of the National Academy of Sciences, vol. 120, no. 12, p. e2214840120, Mar. 2023, doi: 10.1073/PNAS.2214840120.
[3] M. Monaro, E. Barakova, and N. Navarin, “Editorial Special Issue Interaction With Artificial Intelligence Systems: New Human-Centered Perspectives and Challenges,” IEEE Trans Hum Mach Syst, vol. 52, no. 3, pp. 326–331, Jun. 2022, doi: 10.1109/THMS.2022.3172516.
[4] A. Kankanhalli, “Artificial intelligence and the role of researchers: Can it replace us?,” Drying Technology, vol. 38, no. 12, pp. 1539–1541, Aug. 2020, doi: 10.1080/07373937.2020.1801562.
[5] L. Chen, P. Chen, and Z. Lin, “Artificial Intelligence in Education: A Review,” IEEE Access, vol. 8, pp. 75264–75278, 2020, doi: 10.1109/ACCESS.2020.2988510.
[6] C. Surianarayanan, J. J. Lawrence, P. R. Chelliah, E. Prakash, and C. Hewage, “Convergence of Artificial Intelligence and Neuro-science towards the Diagnosis of Neurological Disorders—A Scoping Review,” Sensors 2023, Vol. 23, Page 3062, vol. 23, no. 6, p. 3062, Mar. 2023, doi: 10.3390/S23063062.
[7] I. M. Dzyaloshinsky, “Искусственный интеллект: гуманитарная перспектива,” Вестник Новосибирского государственного университета. Серия: История, филология, vol. 21, no. 6, pp. 20–29, Jun. 2022, doi: 10.25205/1818-7919-2022-21-6-20-29.
[8] E. Brynjolfsson, “The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence,” Daedalus, vol. 151, no. 2, pp. 272–287, May 2022, doi: 10.1162/DAED_A_01915.
[9] M. Pantsar, “Developing Artificial Human-Like Arithmetical Intelligence (and Why),” Minds Mach (Dordr), vol. 33, no. 3, pp. 379–396, Sep. 2023, doi: 10.1007/S11023-023-09636-Y/METRICS.
[10] H. Shevlin, K. Vold, M. Crosby, and M. Halina, “The limits of machine intelligence,” EMBO Rep, vol. 20, no. 10, Sep. 2019, doi: 10.15252/EMBR.201949177.
[11] S. Jabeen, X. Li, M. S. Amin, O. Bourahla, S. Li, and A. Jabbar, “A Review on Methods and Applications in Multimodal Deep Learning,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 19, no. 2s, Feb. 2023, doi: 10.1145/3545572.
[12] L. R. Soenksen et al., “Integrated multimodal artificial intelligence framework for healthcare applications,” NPJ Digit Med, vol. 5, no. 1, pp. 1–10, Dec. 2022, doi: 10.1038/S41746-022-00689-4;SUBJMETA.
[13] I. Celik, M. Dindar, H. Muukkonen, and S. Järvelä, “The Promises and Challenges of Artificial Intelligence for Teachers: a Sys-tematic Review of Research,” TechTrends, vol. 66, no. 4, pp. 616–630, Jul. 2022, doi: 10.1007/S11528-022-00715-Y/FIGURES/6.
[14] T. Gillespie, “Content moderation, AI, and the question of scale,” Big Data Soc, vol. 7, no. 2, Jul. 2020, doi: 10.1177/2053951720943234.
[15] V. U. Gongane, M. V. Munot, and A. D. Anuse, “Detection and moderation of detrimental content on social media platforms: current status and future directions,” Soc Netw Anal Min, vol. 12, no. 1, pp. 1–41, Dec. 2022, doi: 10.1007/S13278-022-00951-3/METRICS.
[16] M. D. Fetters and E. B. Rubinstein, “The 3 Cs of Content, Context, and Concepts: A Practical Approach to Recording Unstruc-tured Field Observations,” The Annals of Family Medicine, vol. 17, no. 6, pp. 554–560, Nov. 2019, doi: 10.1370/AFM.2453.
[17] E. Ötleş, C. A. James, K. D. Lomis, and J. O. Woolliscroft, “Teaching artificial intelligence as a fundamental toolset of medicine,” Cell Rep Med, vol. 3, no. 12, p. 100824, Dec. 2022, doi: 10.1016/J.XCRM.2022.100824.
[18] S. Bucci, M. Schwannauer, and N. Berry, “The digital revolution and its impact on mental health care,” Psychology and Psycho-therapy: Theory, Research and Practice, vol. 92, no. 2, pp. 277–297, Jun. 2019, doi: 10.1111/PAPT.12222.
[19] N. Akhtar, N. Khan, S. Qayyum, M. I. Qureshi, and S. S. Hishan, “Efficacy and pitfalls of digital technologies in healthcare ser-vices: A systematic review of two decades,” Front Public Health, vol. 10, p. 869793, Sep. 2022, doi: 10.3389/FPUBH.2022.869793/BIBTEX.