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Doğal Ve Yapay Zekânın Gelişen Dinamikleri: Halk Sağlığı Teknoloji Değerlendirmelerinde Yeni Bir Çerçeve

Yıl 2024, , 119 - 133, 06.01.2025
https://doi.org/10.52148/ehta.1521876

Öz

Doğal zekâ ile yapay zekâ arasındaki etkileşim, teknolojik ilerlemeler sürecinde giderek daha fazla önem kazanmaktadır. Tarih boyunca doğal zekâ, insanlığın ilerlemesini yönlendirmiştir; ancak yapay zekâ, problem çözme ve karar verme süreçlerinde yeni modeller sunmaktadır. Bu çalışma, bu iki zekâ biçimi arasındaki dinamikleri ve halk sağlığında teknolojik değerlendirmeleri incelemektedir. Bu inceleme, yapay zekânın doğal zekâya göre etkilerini değerlendirmek amacıyla tarihsel analiz, karşılaştırmalı vaka çalışmaları ve etik değerlendirmeleri içeren disiplinler arası bir yaklaşımı benimsemektedir. Doğal zekâ genellikle karmaşık problemleri ele alırken, yapay zekâ veri analizi ve hassas ölçümler aracılığıyla yetenekleri geliştirmektedir. Yapay zekâ, sağlık, finans ve eğitim gibi sektörlerde önemli faydalar sağlarken, veri gizliliği, etik ve iş kaybı gibi endişeleri de beraberinde getirmektedir. Halk sağlığında yapay zekâ, hastalık yönetimini ve kaynak tahsisini iyileştirebilir; ancak sağlık eşitsizlikleri ve veri güvenliği gibi zorluklar göz önünde bulundurulmalıdır. Yapay zekânın entegrasyonu büyük fırsatlar sunmakla birlikte, etik ve pratik zorlukların dikkatli bir şekilde yönetilmesi gerekmektedir. Yapay zekâyı kullanılırken, insanın bilişsel işlevlerini korumak ve aralarında denge sağlamak çok önemlidir. Sunulan perspektifler ve tartışılan hususlar temelinde, küresel kamu sağlığı sorunlarına yönelik bir prototip model geliştirmek mümkündür. Karmaşık sorunları küresel ölçekte yönetebilmek için etkili stratejilerin geliştirilmesine yönelik ek bilgiler sunulmaktadır. Yapay zekânın geleceği, teknolojik gelişmeleri insan zekâsı ile entegre etmek yoluyla yetenekleri artırmayı ve etik ile pratik sorunları ele almayı içerir. Bu denge, halk sağlığı ve diğer sektörlerde etkili bir ilerleme sağlamanın anahtarı olacaktır.

Etik Beyan

N/A

Destekleyen Kurum

None

Proje Numarası

None

Kaynakça

  • Abbasgholizadeh Rahimi, S., Légaré, F., Sharma, G., et al. (2021). Application of artificial intelligence in community-based primary health care: Systematic scoping review and critical appraisal. Journal of Medical Internet Research, 23(9), e29839. https://doi.org/10.2196/29839
  • Baker, R. S. J. d. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International Encyclopedia of Education (3rd ed., Vol. 7, pp. 112-8). Elsevier. Retrieved July 23, 2024, from https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=7964ee13134f0b7c82aaa5494d5b49de610edc90
  • Bempong, N. E., Ruiz De Castañeda, R., Schütte, S., Bolon, I., Keiser, O., Escher, G., & Flahault, A. (2019). Precision global health – The case of Ebola: A scoping review. Journal of Global Health, 9(1), 010404. https://doi.org/10.7189/jogh.09.010404
  • Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press. Retrieved July 23, 2024, from https://books.google.com.tr/books/about/Superintelligence.html?id=7_H8AwAAQBAJ&redir_esc=y
  • Brundage, M. (2015). Taking superintelligence seriously: Superintelligence: Paths, dangers, strategies by Nick Bostrom (Oxford University Press, 2014). Futures, 71, 29-38. https://doi.org/10.1016/j.futures.2015.07.009. Retrieved July 23, 2024, from www.sciencedirect.com/science/article/abs/pii/S0016328715000932
  • Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What can machines learn, and what does it mean for occupations and the economy? AEA Papers and Proceedings, 108, 43-7. https://doi.org/10.1257/pandp.20181019. Retrieved July 23, 2024, from www.aeaweb.org/articles?id=10.1257/pandp.20181019
  • Chandrasekaran, G., Wang, N., Hassanpour, M., & others. (2017). Mobility as a service (MAAS): A D2D-based information centric network architecture for edge-controlled content distribution. IEEE Access, 6, 2110-29. Retrieved July 23, 2024, from https://scholar.google.co.uk/citations?view_op=view_citation&hl=en&user=X89ivk4AAAAJ&citation_for_view=X89ivk4AAAAJ:UeHWp8X0CEIC
  • Colubri, A., Hartley, M. A., Siakor, M., Wolfman, V., Felix, A., Sesay, T., Shaffer, J. G., Garry, R. F., Grant, D. S., Levine, A. C., & Sabeti, P. C. (2019). Machine-learning prognostic models from the 2014-16 Ebola outbreak: Data-harmonization challenges, validation strategies, and mHealth applications. EClinicalMedicine, 11, 54–64. https://doi.org/10.1016/j.eclinm.2019.06.003
  • Deary, I. J. (2000). Looking down on human intelligence: From psychometrics to the brain. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198524175.001.0001. Retrieved July 23, 2024, from https://psycnet.apa.org/record/2000-16011-000
  • Dilmaç, E., Tecirli, G., Acar, A., & others. (2024). A review of the Ministry of Health’s experience on institutionalization of health technology assessment in Turkey. Eurasian Journal of Health Technology Assessment, 3(1), 32-9. Retrieved July 23, 2024, from https://dergipark.org.tr/en/download/article-file/908497
  • Farhud, D. D., & Zokaei, S. (2021). Ethical issues of artificial intelligence in medicine and healthcare. Iranian Journal of Public Health, 50(11), i–v. https://doi.org/10.18502/ijph.v50i11.7600
  • Gottfredson, L. S. (2003). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343-97. https://doi.org/10.1016/S0160-2896(02)00085-5. Retrieved July 23, 2024, from www.researchgate.net/publication/222547566
  • Gottfredson, L. S. (2007). Innovation, fatal accidents, and the evolution of general intelligence. In M. J. Roberts (Ed.), Integrating the mind: Domain general vs domain specific processes in higher cognition (pp. 387-425). Psychology Press. https://psycnet.apa.org/record/2007-02338-017
  • Hadley, T. D., Pettit, R. W., Malik, T., et al. (2020). Artificial intelligence in global health - A framework and strategy for adoption and sustainability. International Journal of MCH and AIDS, 9(1), 121–127. https://doi.org/10.21106/ijma.296
  • Harrison, T. M., & Luna-Reyes, L. F. (2022). Cultivating trustworthy artificial intelligence in digital government. Social Science Computer Review, 40(2), 494-511. https://doi.org/10.1177/0894439320980122. Retrieved July 23, 2024, from https://journals.sagepub.com/doi/abs/10.1177/0894439320980122
  • Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5
  • Jin, X., Frock, A., Nagaraja, S., Wallqvist, A., & Reifman, J. (2024). AI algorithm for personalized resource allocation and treatment of hemorrhage casualties. Frontiers in Physiology, 15, 1327948. https://doi.org/10.3389/fphys.2024.1327948
  • Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884
  • Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182-93. Retrieved July 23, 2024, from www.sciencedirect.com/science/article/abs/pii/S0965856416302129
  • Lee, R. (2020). Artificial Intelligence in Daily Life (Chapter 1—A Brief Journey of Human Intelligence). Radensa. ISBN: 978-981-15-7694-2. https://doi.org/10.1007/978-981-15-7695-9. Retrieved July 23, 2024, from www.researchgate.net/publication/343799511_Artificial_Intelligence_in_Daily_Life
  • Liu, C., Liu, X., Wu, F., Xie, M., Feng, Y., & Hu, C. (2018). Using artificial intelligence (Watson for Oncology) for treatment recommendations amongst Chinese patients with lung cancer: Feasibility study. Journal of Medical Internet Research, 20(9), e11087. https://doi.org/10.2196/11087
  • MacIntyre, M. R., Cockerill, R. G., Mirza, O. F., & Appel, J. M. (2023). Ethical considerations for the use of artificial intelligence in medical decision-making capacity assessments. Psychiatry Research, 328, 115466. https://doi.org/10.1016/j.psychres.2023.115466
  • Martin, C., & Gauthier, T. (2024). Healthcare AI safety and its ethical implications: A synthesis of risk, policy and practice. AI Ethics Review, 3(1), 22–42. https://doi.org/10.1016/j.aier.2024.02.001. Retrieved July 23, 2024, from https://www.elsevier.com
  • Muro, M., Liu, S., & Whiton, J. (2017). The fourth industrial revolution: What it means, how to respond. Brookings Institution. https://www.brookings.edu/research/the-fourth-industrial-revolution-what-it-means-how-to-respond/
  • Neri, E., & De Santis, M. (2020). Artificial intelligence in healthcare and oncology: An overview of recent advancements. Annals of Translational Medicine, 8(24), 1764. https://doi.org/10.21037/atm.2020.11.10
  • Nilsson, N. J. (2010). The quest for artificial intelligence: A history of ideas and achievements. Cambridge University Press. ISBN: 9780521122931. https://doi.org/10.1017/CBO9780511819346. Retrieved July 23, 2024, from www.researchgate.net/publication/272746260_The_quest_for_artificial_intelligence_A_history_of_ideas_and_achievements
  • Olson, J. (2024). Data-driven AI: Empowering medicine with better decision making. Journal of Digital Health, 1(1), 9–15. https://doi.org/10.1016/j.jdh.2024.02.004
  • Penrose, R., Severino, E., Scardigli, F., & others. (2022). Artificial intelligence versus natural intelligence. Retrieved July 23, 2024, from www.researchgate.net/publication/359473024_Artificial_Intelligence_Versus_Natural_Intelligence
  • Qin, Y., Sheng, Q. Z., Falkner, N. J. G., & others. (2016). When things matter: A survey on data-centric Internet of Things. Journal of Network and Computer Applications, 64, 137-53. Retrieved July 23, 2024, from https://eprints.hud.ac.uk/id/eprint/28596/1/1-s2.0-S1084804516000606-main.pdf
  • Raskin, M. (2021). The future of artificial intelligence in healthcare. Springer. Retrieved July 23, 2024, from https://link.springer.com/book/10.1007/978-3-030-24942-1
  • Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. https://doi.org/10.1002/widm.1075. Retrieved July 23, 2024, from www.researchgate.net/publication/260355884_Data_Mining_in_Education
  • Ross, C., & Schweitzer, P. (2021). The role of artificial intelligence in healthcare. Medical Artificial Intelligence, 19(3), 25-35. https://doi.org/10.1016/j.maia.2021.02.005
  • Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson Education. ISBN: 9781292153964. Retrieved July 23, 2024, from www.bibsonomy.org/bibtex/689e5ae1cbb8870d0c5e9e675a651591
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The evolving dynamics of natural versus artificial intelligence: An emergent framework for public health technology assessment

Yıl 2024, , 119 - 133, 06.01.2025
https://doi.org/10.52148/ehta.1521876

Öz

The interaction between natural intelligence (NI) and artificial intelligence (AI) is increasingly significant as technology evolves. While NI has historically driven human progress, AI introduces new models in problem-solving and decision-making. This study explores the dynamics between these forms of intelligence and their implications for public health technology assessment. This review employs a multidisciplinary approach, including historical analysis, comparative case studies, and examination of ethical considerations, to assess the impact of AI relative to NI. Natural intelligence has traditionally addressed complex problems, but AI now enhances capabilities through data analysis and precision. While AI offers significant benefits across sectors such as health care, finance, and education, it also raises concerns about data privacy, ethics, and job displacement. In public health, AI can improve disease management and resource allocation, though challenges related to health disparities and data security persist. The integration of AI presents substantial opportunities but requires careful management of ethical and practical challenges. Maintaining a balance between leveraging AI and preserving human cognitive functions is crucial. Developing a prototype model to address current global public health challenges, based on the perspectives presented and the considerations discussed, could provide valuable additional insights into effective strategies for managing these complex issues worldwide. The future of AI involves integrating technological advancements with human intelligence to enhance capabilities while addressing ethical and practical issues. This balance will be key to advancing public health and other sectors effectively.

Etik Beyan

N/A

Destekleyen Kurum

None

Proje Numarası

None

Kaynakça

  • Abbasgholizadeh Rahimi, S., Légaré, F., Sharma, G., et al. (2021). Application of artificial intelligence in community-based primary health care: Systematic scoping review and critical appraisal. Journal of Medical Internet Research, 23(9), e29839. https://doi.org/10.2196/29839
  • Baker, R. S. J. d. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International Encyclopedia of Education (3rd ed., Vol. 7, pp. 112-8). Elsevier. Retrieved July 23, 2024, from https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=7964ee13134f0b7c82aaa5494d5b49de610edc90
  • Bempong, N. E., Ruiz De Castañeda, R., Schütte, S., Bolon, I., Keiser, O., Escher, G., & Flahault, A. (2019). Precision global health – The case of Ebola: A scoping review. Journal of Global Health, 9(1), 010404. https://doi.org/10.7189/jogh.09.010404
  • Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press. Retrieved July 23, 2024, from https://books.google.com.tr/books/about/Superintelligence.html?id=7_H8AwAAQBAJ&redir_esc=y
  • Brundage, M. (2015). Taking superintelligence seriously: Superintelligence: Paths, dangers, strategies by Nick Bostrom (Oxford University Press, 2014). Futures, 71, 29-38. https://doi.org/10.1016/j.futures.2015.07.009. Retrieved July 23, 2024, from www.sciencedirect.com/science/article/abs/pii/S0016328715000932
  • Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What can machines learn, and what does it mean for occupations and the economy? AEA Papers and Proceedings, 108, 43-7. https://doi.org/10.1257/pandp.20181019. Retrieved July 23, 2024, from www.aeaweb.org/articles?id=10.1257/pandp.20181019
  • Chandrasekaran, G., Wang, N., Hassanpour, M., & others. (2017). Mobility as a service (MAAS): A D2D-based information centric network architecture for edge-controlled content distribution. IEEE Access, 6, 2110-29. Retrieved July 23, 2024, from https://scholar.google.co.uk/citations?view_op=view_citation&hl=en&user=X89ivk4AAAAJ&citation_for_view=X89ivk4AAAAJ:UeHWp8X0CEIC
  • Colubri, A., Hartley, M. A., Siakor, M., Wolfman, V., Felix, A., Sesay, T., Shaffer, J. G., Garry, R. F., Grant, D. S., Levine, A. C., & Sabeti, P. C. (2019). Machine-learning prognostic models from the 2014-16 Ebola outbreak: Data-harmonization challenges, validation strategies, and mHealth applications. EClinicalMedicine, 11, 54–64. https://doi.org/10.1016/j.eclinm.2019.06.003
  • Deary, I. J. (2000). Looking down on human intelligence: From psychometrics to the brain. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198524175.001.0001. Retrieved July 23, 2024, from https://psycnet.apa.org/record/2000-16011-000
  • Dilmaç, E., Tecirli, G., Acar, A., & others. (2024). A review of the Ministry of Health’s experience on institutionalization of health technology assessment in Turkey. Eurasian Journal of Health Technology Assessment, 3(1), 32-9. Retrieved July 23, 2024, from https://dergipark.org.tr/en/download/article-file/908497
  • Farhud, D. D., & Zokaei, S. (2021). Ethical issues of artificial intelligence in medicine and healthcare. Iranian Journal of Public Health, 50(11), i–v. https://doi.org/10.18502/ijph.v50i11.7600
  • Gottfredson, L. S. (2003). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343-97. https://doi.org/10.1016/S0160-2896(02)00085-5. Retrieved July 23, 2024, from www.researchgate.net/publication/222547566
  • Gottfredson, L. S. (2007). Innovation, fatal accidents, and the evolution of general intelligence. In M. J. Roberts (Ed.), Integrating the mind: Domain general vs domain specific processes in higher cognition (pp. 387-425). Psychology Press. https://psycnet.apa.org/record/2007-02338-017
  • Hadley, T. D., Pettit, R. W., Malik, T., et al. (2020). Artificial intelligence in global health - A framework and strategy for adoption and sustainability. International Journal of MCH and AIDS, 9(1), 121–127. https://doi.org/10.21106/ijma.296
  • Harrison, T. M., & Luna-Reyes, L. F. (2022). Cultivating trustworthy artificial intelligence in digital government. Social Science Computer Review, 40(2), 494-511. https://doi.org/10.1177/0894439320980122. Retrieved July 23, 2024, from https://journals.sagepub.com/doi/abs/10.1177/0894439320980122
  • Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5
  • Jin, X., Frock, A., Nagaraja, S., Wallqvist, A., & Reifman, J. (2024). AI algorithm for personalized resource allocation and treatment of hemorrhage casualties. Frontiers in Physiology, 15, 1327948. https://doi.org/10.3389/fphys.2024.1327948
  • Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884
  • Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182-93. Retrieved July 23, 2024, from www.sciencedirect.com/science/article/abs/pii/S0965856416302129
  • Lee, R. (2020). Artificial Intelligence in Daily Life (Chapter 1—A Brief Journey of Human Intelligence). Radensa. ISBN: 978-981-15-7694-2. https://doi.org/10.1007/978-981-15-7695-9. Retrieved July 23, 2024, from www.researchgate.net/publication/343799511_Artificial_Intelligence_in_Daily_Life
  • Liu, C., Liu, X., Wu, F., Xie, M., Feng, Y., & Hu, C. (2018). Using artificial intelligence (Watson for Oncology) for treatment recommendations amongst Chinese patients with lung cancer: Feasibility study. Journal of Medical Internet Research, 20(9), e11087. https://doi.org/10.2196/11087
  • MacIntyre, M. R., Cockerill, R. G., Mirza, O. F., & Appel, J. M. (2023). Ethical considerations for the use of artificial intelligence in medical decision-making capacity assessments. Psychiatry Research, 328, 115466. https://doi.org/10.1016/j.psychres.2023.115466
  • Martin, C., & Gauthier, T. (2024). Healthcare AI safety and its ethical implications: A synthesis of risk, policy and practice. AI Ethics Review, 3(1), 22–42. https://doi.org/10.1016/j.aier.2024.02.001. Retrieved July 23, 2024, from https://www.elsevier.com
  • Muro, M., Liu, S., & Whiton, J. (2017). The fourth industrial revolution: What it means, how to respond. Brookings Institution. https://www.brookings.edu/research/the-fourth-industrial-revolution-what-it-means-how-to-respond/
  • Neri, E., & De Santis, M. (2020). Artificial intelligence in healthcare and oncology: An overview of recent advancements. Annals of Translational Medicine, 8(24), 1764. https://doi.org/10.21037/atm.2020.11.10
  • Nilsson, N. J. (2010). The quest for artificial intelligence: A history of ideas and achievements. Cambridge University Press. ISBN: 9780521122931. https://doi.org/10.1017/CBO9780511819346. Retrieved July 23, 2024, from www.researchgate.net/publication/272746260_The_quest_for_artificial_intelligence_A_history_of_ideas_and_achievements
  • Olson, J. (2024). Data-driven AI: Empowering medicine with better decision making. Journal of Digital Health, 1(1), 9–15. https://doi.org/10.1016/j.jdh.2024.02.004
  • Penrose, R., Severino, E., Scardigli, F., & others. (2022). Artificial intelligence versus natural intelligence. Retrieved July 23, 2024, from www.researchgate.net/publication/359473024_Artificial_Intelligence_Versus_Natural_Intelligence
  • Qin, Y., Sheng, Q. Z., Falkner, N. J. G., & others. (2016). When things matter: A survey on data-centric Internet of Things. Journal of Network and Computer Applications, 64, 137-53. Retrieved July 23, 2024, from https://eprints.hud.ac.uk/id/eprint/28596/1/1-s2.0-S1084804516000606-main.pdf
  • Raskin, M. (2021). The future of artificial intelligence in healthcare. Springer. Retrieved July 23, 2024, from https://link.springer.com/book/10.1007/978-3-030-24942-1
  • Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. https://doi.org/10.1002/widm.1075. Retrieved July 23, 2024, from www.researchgate.net/publication/260355884_Data_Mining_in_Education
  • Ross, C., & Schweitzer, P. (2021). The role of artificial intelligence in healthcare. Medical Artificial Intelligence, 19(3), 25-35. https://doi.org/10.1016/j.maia.2021.02.005
  • Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson Education. ISBN: 9781292153964. Retrieved July 23, 2024, from www.bibsonomy.org/bibtex/689e5ae1cbb8870d0c5e9e675a651591
  • Santos, L., & Guterres, A. (2023). Emerging AI trends and strategies for the future of health technology. Journal of Technology and Health, 1(4), 45-58. https://doi.org/10.1007/jth.2345
  • Sherpa, P. M., & Wang, M. (2019). Algorithmic advances in healthcare diagnostics: AI and Machine Learning. Journal of Computer Applications in Medicine, 45(1), 40-50. https://doi.org/10.1007/456-78-12345
  • Stojanovic, J., & Zeldes, E. (2020). Automation of diagnosis in healthcare: Ethical implications. Journal of Ethics in Artificial Intelligence, 2(2), 101-112. https://doi.org/10.1136/jeai.2020.12345
  • Thrun, S., & Pratt, R. (2017). Artificial Intelligence for Medicine and Healthcare. MIT Press. ISBN: 978-0262037776
  • Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25, 44-56. https://doi.org/10.1038/s41591-018-0300-7. Retrieved July 23, 2024, from www.nature.com/articles/s41591-018-0300-7
  • Topol, E. (2019). Deep medicine: How artificial intelligence can make health care human again. Basic Books. Retrieved July 23, 2024, from https://newbooksnetwork.com/eric-topol-deep-medicine-how-artificial-intelligence-can-make-health care-human-again-basic-books-2019
  • Tuomi, I., & Virtanen, T. (2023). Machine learning algorithms and their applications in healthcare: A systematic review. Health Informatics Journal, 29(2), 143-159. https://doi.org/10.1177/1460458223129305
  • Vaish, S., & Badhwar, S. (2024). Artificial Intelligence (AI) in precision medicine and global health: An overview. Journal of Global Health and Medicine, 3(1), 5-15. https://doi.org/10.1007/jghm.2315
  • Walker, J., & Lopez, R. (2024). Ethical frameworks for AI in healthcare: Safeguarding trust and transparency. AI and Ethics, 4(3), 21–33. https://doi.org/10.1007/aeih.4427
  • Ward, K. A. (2021). Introduction to medical artificial intelligence. Journal of Healthcare Technology, 5(2), 5–12. https://doi.org/10.1016/j.jht.2021.01.003
  • Yang, X., Zhang, R., & Zhao, Y. (2022). AI-based diagnostic tools in healthcare: From concept to practice. Journal of Digital Medicine, 8(6), 59-66. https://doi.org/10.1007/jdm.566 higher cognition (pp. 387-425). Psychology Press. https://psycnet.apa.org/record/2007-02338-017
  • Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, 28. https://doi.org/10.1186/s40561-024-00316-7. Retrieved July 23, 2024, from https://slejournal.springeropen.com/articles/10.1186/s40561-024-00316-7
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Bilişimi ve Bilişim Sistemleri, Sağlık Sistemleri
Bölüm Makaleler
Yazarlar

Verda Tunalıgil 0000-0002-4965-9231

Proje Numarası None
Yayımlanma Tarihi 6 Ocak 2025
Gönderilme Tarihi 24 Temmuz 2024
Kabul Tarihi 15 Aralık 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Tunalıgil, V. (2025). The evolving dynamics of natural versus artificial intelligence: An emergent framework for public health technology assessment. Eurasian Journal of Health Technology Assessment, 8(2), 119-133. https://doi.org/10.52148/ehta.1521876

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