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Yapay Zeka Destekli Meme Görüntüleme Araçlarının Benimsenmesini Etkileyen Biyokültürel Faktörler: Kanser Bakımında Teknoloji Uygulamasına İlişkin Biyolojik Perspektifler

Yıl 2025, Cilt: 11 Sayı: 1, 567 - 581, 30.08.2025

Öz

Meme kanseri, dünya çapında kadınlarda ölümlerin başlıca nedenlerinden biri haline gelmiştir. Buna bağlı olarak, meme kanserinin teşhisine vurgu yapılmış ve meme kanserinin teşhisinde önemli ilerlemeler için AI görüntüleme araçlarının entegrasyonu vurgulanmıştır. Bu çalışma, AI destekli meme görüntüleme araçlarının benimsenmesini etkileyen biyokültürel faktörlerin analizine odaklanmaktadır. Bu amaca ulaşmak için, Türkiye bağlamında 12 onkoloji uzmanıyla görüşmeler yapılmış ve NVivo yazılımı kullanılarak tematik analiz yapılmıştır. Meme görüntüleme yolunda AI, meme kanserinin biyobelirteçlerinde AI, AI destekli meme görüntüleme araçlarının benimsenmesini etkileyen biyokültürel faktörler, genetik değişimin tahmini ve etik kısıtlamalar gibi önemli temalar formüle edilmiştir. Meme görüntüleme yolunda AI'nın büyük ölçüde klinisyenler için teşhis süresini azaltmak için teşvik edilmesine rağmen, etik kısıtlamalar gibi başka dezavantajları da olduğu gözlemlenmiştir.

Kaynakça

  • A. Agrawal, G. D. K., B. Khurana, A.D. Sodickson, Y. Liang, D. Dreizin. (2023). A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations. Emerg Radiol, 30 (3) (2023), pp. 267-277, 10.1007/s10140-023-02121-0.
  • A.V. Prakash, S. D. (2021). Medical practitioner’s adoption of intelligent clinical diagnostic decision support systems: a mixed-methods study. Inf Manage, 58 (7) (2021), Article 1035242021/11/01/https://doi.org/10.1016/j.im.2021.1035.
  • Adeniji, A. A., Dulal, S., & Martin, M. G. (2021). Personalized medicine in oncology in the developing world: barriers and concepts to improve status quo. World journal of oncology, 12(2-3), 50. https://doi.org/https://doi.org/10.14740%2Fwjon1345
  • Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010. https://doi.org/https://doi.org/10.1093/database/baaa010
  • Ahn, J. S., Shin, S., Yang, S.-A., Park, E. K., Kim, K. H., Cho, S. I., Ock, C.-Y., & Kim, S. (2023). Artificial intelligence in breast cancer diagnosis and personalized medicine. Journal of Breast Cancer, 26(5), 405. https://doi.org/https://doi.org/10.4048%2Fjbc.2023.26.e45
  • Alam, L., & Mueller, S. (2021). Examining the effect of explanation on satisfaction and trust in AI diagnostic systems. BMC medical informatics and decision making, 21(1), 178.
  • Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., & Salhi, A. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156-191. https://doi.org/https://doi.org/10.1016/j.inffus.2023.03.008
  • Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A.,
  • Alrashed, M., Bin Saleh, K., & Badreldin, H. A. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689.
  • Arabi, H., AkhavanAllaf, A., Sanaat, A., Shiri, I., & Zaidi, H. (2021). The promise of artificial intelligence and deep learning in PET and SPECT imaging. Physica Medica, 83, 122-137. https://doi.org/https://doi.org/10.1016/j.ejmp.2021.03.008
  • Berrones Reyes, M. C. (2024). Use of artificial intelligence tools for computational-aided diagnostics Universidad Autónoma de Nuevo León].
  • Bhushan, A., Gonsalves, A., & Menon, J. U. (2021). Current state of breast cancer diagnosis, treatment, and theranostics. Pharmaceutics, 13(5), 723. https://doi.org/https://doi.org/10.3390/pharmaceutics13050723
  • Cè, M., Caloro, E., Pellegrino, M. E., Basile, M., Sorce, A., Fazzini, D., Oliva, G., & Cellina, M. (2022). Artificial intelligence in breast cancer imaging: Risk stratification, lesion detection and classification, treatment planning and prognosis—A narrative review. Exploration of Targeted Anti-tumor Therapy, 3(6), 795. https://doi.org/https://doi.org/10.37349%2Fetat.2022.00113
  • Champendal, M., Marmy, L., Malamateniou, C., & Dos Reis, C. S. (2023). Artificial intelligence to support person-centred care in breast imaging-A scoping review. Journal of medical imaging and radiation sciences, 54(3), 511-544. https://doi.org/https://doi.org/10.1016/j.jmir.2023.04.001
  • Cheverko, C. M., Marklein, K. E., Clark, M. A., Mayus, R. C., McGuire, S. A., Turner-Byfield, E., Green, M. K., Weiss, N. M., Lagan, E. M., & Hubbe, M. (2020). Beyond the Biocultural Approach: A Quantitative Assessment of the Use of Theory in Bioarchaeological Assessment of the Use of Theory in Bioarchaeological Literature from 2007 to 2018. Bioarchaeology International, 4(1), 37. https://doi.org/10.5744/bi.2020.1002
  • Djafar, H., Yunus, R., Pomalato, S. W. D., & Rasid, R. (2021). Qualitative and quantitative paradigm constellation in educational research methodology. International Journal of Educational Research & Social Sciences, 2(2), 339-345. https://doi.org/https://doi.org/10.51601/ijersc.v2i2.70
  • Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and structural biotechnology journal, 18, 2300-2311. https://doi.org/https://doi.org/10.1016/j.csbj.2020.08.019
  • Durur-Subasi, I., & Özçelik, Ş. B. (2023). Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal–Ethical Considerations. The Eurasian Journal of Medicine, 55(1), S114. https://doi.org/https://doi.org/10.5152%2Feurasianjmed.2023.23360
  • Forghani, R. (2020). Precision digital oncology: emerging role of radiomics-based biomarkers and artificial intelligence for advanced imaging and characterization of brain tumors. Radiology: Imaging Cancer, 2(4), e190047. https://doi.org/https://doi.org/10.1148/rycan.2020190047
  • Ginsburg, O., Yip, C. H., Brooks, A., Cabanes, A., Caleffi, M., Dunstan Yataco, J. A., Gyawali, B., McCormack, V., McLaughlin de Anderson, M., & Mehrotra, R. (2020). Breast cancer early detection: A phased approach to implementation. Cancer, 126, 2379-2393. https://doi.org/ https://doi.org/10.1002/cncr.32887
  • Heinisch, B. (2020). Knowledge translation and its interrelation with usability and accessibility. Biocultural diversity translated by means of technology and language—the case of citizen science contributing to the sustainable development goals. Sustainability, 13(1), 54. https://doi.org/https://doi.org/10.3390/su13010054
  • Hennink, M., Hutter, I., & Bailey, A. (2020). Qualitative research methods. Sage.
  • Hickman, S. E., Baxter, G. C., & Gilbert, F. J. (2021). Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. British journal of cancer, 125(1), 15-22. https://doi.org/https://doi.org/10.1038%2Fs41416-021-01333-w
  • Hollander, J. A., Cory-Slechta, D. A., Jacka, F. N., Szabo, S. T., Guilarte, T. R., Bilbo, S. D., Mattingly, C. J., Moy, S.
  • S., Haroon, E., & Hornig, M. (2020). Beyond the looking glass: recent advances in understanding the impact of environmental exposures on neuropsychiatric disease. Neuropsychopharmacology, 45(7), 1086-1096. https://doi.org/https://doi.org/10.1038/s41386-020-0648-5
  • Huisman M, R. E., Parker W, et al. (2021). An international survey on AI in radiology in 1041 radiologists and radiology residents, part 2: expectations,hurdles to implementation, and education. . European Radiology 2021;31(11): 8797–8806.
  • Jeong, J. J. V., B.L.; Bhimireddy, A.; Kim, T.; Santos, T.; Correa, R.; Dutt, R.; Mosunjac, M.; Oprea-Ilies, G.; Smith, G.; et al. (2023). The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic
  • Mammographic Images. . Radiol. Artif. Intell. 2023, 5, e220047. . Karger, E., & Kureljusic, M. (2023). Artificial intelligence for cancer detection—a bibliometric analysis and avenues for future research. Current Oncology, 30(2), 1626-1647. https://doi.org/https://doi.org/10.3390/curroncol30020125
  • Khan, M., Shiwlani, A., Qayyum, M. U., Sherani, A. M. K., & Hussain, H. K. (2024). AI-powered healthcare revolution: an extensive examination of innovative methods in cancer treatment. BULLET: Jurnal Multidisiplin Ilmu, 3(1), 87-98.
  • Kondylakis, H., Axenie, C., Bastola, D., Katehakis, D. G., Kouroubali, A., Kurz, D., Larburu, N., Macía, I., Maguire, R., & Maramis, C. (2020). Status and recommendations of technological and data-driven innovations in cancer care: Focus group study. Journal of medical Internet research, 22(12), e22034. https://doi.org/https://doi.org/10.2196/22034
  • Li, H., & Giger, M. L. (2021). Artificial intelligence and interpretations in breast cancer imaging. In Artificial intelligence in medicine (pp. 291-308). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-821259-2.00015-6
  • Li, M., Xiong, X., Xu, B., & Dickson, C. (2024). Chinese Oncologists’ Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study. JMIR Formative Research, 8(1), e53918. https://doi.org/https://doi.org/10.2196/53918
  • Morrison, K. (2021). Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption. Future Healthc J, 8 (3) (2021), Article e648, 10.7861/fhj.2020-0258.
  • Mridha, K., Kumbhani, S., Jha, S., Joshi, D., Ghosh, A., & Shaw, R. N. (2021). Deep learning algorithms are used to automatically detection invasive ducal carcinoma in whole slide images. 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA),
  • Ozuem, W., Willis, M., & Howell, K. (2022). Thematic analysis without paradox: sensemaking and context. Qualitative Market Research: An International Journal, 25(1), 143-157. https://doi.org/https://doi.org/10.1108/QMR-07-2021-0092
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BIOCULTURAL FACTORS INFLUENCING THE ADOPTION OF AI-POWERED BREAST IMAGING TOOLS: BIOLOGICAL PERSPECTIVES ON TECHNOLOGY IMPLEMENTATION IN CANCER CARE

Yıl 2025, Cilt: 11 Sayı: 1, 567 - 581, 30.08.2025

Öz

Breast cancer has become one of the major reasons of mortality in women around the world. As a result, emphasis has been given on the diagnosis of breast cancer, emphasizing the integration of AI imaging tools for important advancements within the diagnosis of breast cancer. This study focuses on analysing the biocultural factors which influence the adoption of AI-powered breast imaging tools. In order to address this aim, interviews were conducted with 12 oncology specialists within the context of Turkey and thematic analysis was conducted by using NVivo software. Important themes were formulated which include AI in breast imaging pathway, AI in breast cancer’s biomarkers, biocultural factors influencing adoption of AI-powered breast imaging tools, prediction of genetic alteration and ethical constraints. It has been observed that although AI in breast imaging pathway is largely being promoted to reduce diagnosis time for the clinicians, it also has other disadvantages such as ethical constraints.

Kaynakça

  • A. Agrawal, G. D. K., B. Khurana, A.D. Sodickson, Y. Liang, D. Dreizin. (2023). A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations. Emerg Radiol, 30 (3) (2023), pp. 267-277, 10.1007/s10140-023-02121-0.
  • A.V. Prakash, S. D. (2021). Medical practitioner’s adoption of intelligent clinical diagnostic decision support systems: a mixed-methods study. Inf Manage, 58 (7) (2021), Article 1035242021/11/01/https://doi.org/10.1016/j.im.2021.1035.
  • Adeniji, A. A., Dulal, S., & Martin, M. G. (2021). Personalized medicine in oncology in the developing world: barriers and concepts to improve status quo. World journal of oncology, 12(2-3), 50. https://doi.org/https://doi.org/10.14740%2Fwjon1345
  • Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010. https://doi.org/https://doi.org/10.1093/database/baaa010
  • Ahn, J. S., Shin, S., Yang, S.-A., Park, E. K., Kim, K. H., Cho, S. I., Ock, C.-Y., & Kim, S. (2023). Artificial intelligence in breast cancer diagnosis and personalized medicine. Journal of Breast Cancer, 26(5), 405. https://doi.org/https://doi.org/10.4048%2Fjbc.2023.26.e45
  • Alam, L., & Mueller, S. (2021). Examining the effect of explanation on satisfaction and trust in AI diagnostic systems. BMC medical informatics and decision making, 21(1), 178.
  • Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., & Salhi, A. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156-191. https://doi.org/https://doi.org/10.1016/j.inffus.2023.03.008
  • Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A.,
  • Alrashed, M., Bin Saleh, K., & Badreldin, H. A. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689.
  • Arabi, H., AkhavanAllaf, A., Sanaat, A., Shiri, I., & Zaidi, H. (2021). The promise of artificial intelligence and deep learning in PET and SPECT imaging. Physica Medica, 83, 122-137. https://doi.org/https://doi.org/10.1016/j.ejmp.2021.03.008
  • Berrones Reyes, M. C. (2024). Use of artificial intelligence tools for computational-aided diagnostics Universidad Autónoma de Nuevo León].
  • Bhushan, A., Gonsalves, A., & Menon, J. U. (2021). Current state of breast cancer diagnosis, treatment, and theranostics. Pharmaceutics, 13(5), 723. https://doi.org/https://doi.org/10.3390/pharmaceutics13050723
  • Cè, M., Caloro, E., Pellegrino, M. E., Basile, M., Sorce, A., Fazzini, D., Oliva, G., & Cellina, M. (2022). Artificial intelligence in breast cancer imaging: Risk stratification, lesion detection and classification, treatment planning and prognosis—A narrative review. Exploration of Targeted Anti-tumor Therapy, 3(6), 795. https://doi.org/https://doi.org/10.37349%2Fetat.2022.00113
  • Champendal, M., Marmy, L., Malamateniou, C., & Dos Reis, C. S. (2023). Artificial intelligence to support person-centred care in breast imaging-A scoping review. Journal of medical imaging and radiation sciences, 54(3), 511-544. https://doi.org/https://doi.org/10.1016/j.jmir.2023.04.001
  • Cheverko, C. M., Marklein, K. E., Clark, M. A., Mayus, R. C., McGuire, S. A., Turner-Byfield, E., Green, M. K., Weiss, N. M., Lagan, E. M., & Hubbe, M. (2020). Beyond the Biocultural Approach: A Quantitative Assessment of the Use of Theory in Bioarchaeological Assessment of the Use of Theory in Bioarchaeological Literature from 2007 to 2018. Bioarchaeology International, 4(1), 37. https://doi.org/10.5744/bi.2020.1002
  • Djafar, H., Yunus, R., Pomalato, S. W. D., & Rasid, R. (2021). Qualitative and quantitative paradigm constellation in educational research methodology. International Journal of Educational Research & Social Sciences, 2(2), 339-345. https://doi.org/https://doi.org/10.51601/ijersc.v2i2.70
  • Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and structural biotechnology journal, 18, 2300-2311. https://doi.org/https://doi.org/10.1016/j.csbj.2020.08.019
  • Durur-Subasi, I., & Özçelik, Ş. B. (2023). Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal–Ethical Considerations. The Eurasian Journal of Medicine, 55(1), S114. https://doi.org/https://doi.org/10.5152%2Feurasianjmed.2023.23360
  • Forghani, R. (2020). Precision digital oncology: emerging role of radiomics-based biomarkers and artificial intelligence for advanced imaging and characterization of brain tumors. Radiology: Imaging Cancer, 2(4), e190047. https://doi.org/https://doi.org/10.1148/rycan.2020190047
  • Ginsburg, O., Yip, C. H., Brooks, A., Cabanes, A., Caleffi, M., Dunstan Yataco, J. A., Gyawali, B., McCormack, V., McLaughlin de Anderson, M., & Mehrotra, R. (2020). Breast cancer early detection: A phased approach to implementation. Cancer, 126, 2379-2393. https://doi.org/ https://doi.org/10.1002/cncr.32887
  • Heinisch, B. (2020). Knowledge translation and its interrelation with usability and accessibility. Biocultural diversity translated by means of technology and language—the case of citizen science contributing to the sustainable development goals. Sustainability, 13(1), 54. https://doi.org/https://doi.org/10.3390/su13010054
  • Hennink, M., Hutter, I., & Bailey, A. (2020). Qualitative research methods. Sage.
  • Hickman, S. E., Baxter, G. C., & Gilbert, F. J. (2021). Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. British journal of cancer, 125(1), 15-22. https://doi.org/https://doi.org/10.1038%2Fs41416-021-01333-w
  • Hollander, J. A., Cory-Slechta, D. A., Jacka, F. N., Szabo, S. T., Guilarte, T. R., Bilbo, S. D., Mattingly, C. J., Moy, S.
  • S., Haroon, E., & Hornig, M. (2020). Beyond the looking glass: recent advances in understanding the impact of environmental exposures on neuropsychiatric disease. Neuropsychopharmacology, 45(7), 1086-1096. https://doi.org/https://doi.org/10.1038/s41386-020-0648-5
  • Huisman M, R. E., Parker W, et al. (2021). An international survey on AI in radiology in 1041 radiologists and radiology residents, part 2: expectations,hurdles to implementation, and education. . European Radiology 2021;31(11): 8797–8806.
  • Jeong, J. J. V., B.L.; Bhimireddy, A.; Kim, T.; Santos, T.; Correa, R.; Dutt, R.; Mosunjac, M.; Oprea-Ilies, G.; Smith, G.; et al. (2023). The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic
  • Mammographic Images. . Radiol. Artif. Intell. 2023, 5, e220047. . Karger, E., & Kureljusic, M. (2023). Artificial intelligence for cancer detection—a bibliometric analysis and avenues for future research. Current Oncology, 30(2), 1626-1647. https://doi.org/https://doi.org/10.3390/curroncol30020125
  • Khan, M., Shiwlani, A., Qayyum, M. U., Sherani, A. M. K., & Hussain, H. K. (2024). AI-powered healthcare revolution: an extensive examination of innovative methods in cancer treatment. BULLET: Jurnal Multidisiplin Ilmu, 3(1), 87-98.
  • Kondylakis, H., Axenie, C., Bastola, D., Katehakis, D. G., Kouroubali, A., Kurz, D., Larburu, N., Macía, I., Maguire, R., & Maramis, C. (2020). Status and recommendations of technological and data-driven innovations in cancer care: Focus group study. Journal of medical Internet research, 22(12), e22034. https://doi.org/https://doi.org/10.2196/22034
  • Li, H., & Giger, M. L. (2021). Artificial intelligence and interpretations in breast cancer imaging. In Artificial intelligence in medicine (pp. 291-308). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-821259-2.00015-6
  • Li, M., Xiong, X., Xu, B., & Dickson, C. (2024). Chinese Oncologists’ Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study. JMIR Formative Research, 8(1), e53918. https://doi.org/https://doi.org/10.2196/53918
  • Morrison, K. (2021). Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption. Future Healthc J, 8 (3) (2021), Article e648, 10.7861/fhj.2020-0258.
  • Mridha, K., Kumbhani, S., Jha, S., Joshi, D., Ghosh, A., & Shaw, R. N. (2021). Deep learning algorithms are used to automatically detection invasive ducal carcinoma in whole slide images. 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA),
  • Ozuem, W., Willis, M., & Howell, K. (2022). Thematic analysis without paradox: sensemaking and context. Qualitative Market Research: An International Journal, 25(1), 143-157. https://doi.org/https://doi.org/10.1108/QMR-07-2021-0092
  • Pacilè, S., Lopez, J., Chone, P., Bertinotti, T., Grouin, J. M., & Fillard, P. (2020). Improving breast cancer detection accuracy of mammography with the concurrent use of an artificial intelligence tool. Radiology: Artificial Intelligence, 2(6), e190208. https://doi.org/https://doi.org/10.1148/ryai.2020190208
  • Pathak, V., Jena, B., & Kalra, S. (2013). Qualitative research. Perspectives in clinical research, 4(3), 192. https://doi.org/10.4103/2229-3485.115389
  • Paul, R., Karmakar, S., & Gupta, P. (2023). Can AI-powered imaging be a replacement for radiologists? Deep Learning in Medical Image Processing and Analysis, 97.
  • Piispanen, J.-R. (2023). Current discourses in artificial intelligence ethics. Porto, A. E. (2020). Adopting e-learning technologies in higher educational institutions: The role of organizational culture, technology acceptance and attitude. Review of Social Sciences, 5(1), 01-11. https://doi.org/https://doi.org/10.18533/rss.v5i1.143
  • Shaikh, K., Krishnan, S., & Thanki, R. M. (2021). Artificial intelligence in breast cancer early detection and diagnosis. Springer. https://doi.org/https://doi.org/10.1007/978-3-030-59208-0
  • Shen, Y., Shamout, F. E., Oliver, J. R., Witowski, J., Kannan, K., Park, J., Wu, N., Huddleston, C., Wolfson, S., & Millet, A. (2021). Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nature communications, 12(1), 5645. https://doi.org/https://doi.org/10.1038/s41467-021-26023-2
  • Smolarz, B., Nowak, A. Z., & Romanowicz, H. (2022). Breast cancer—epidemiology, classification, pathogenesis and treatment (review of literature). Cancers, 14(10), 2569. https://doi.org/https://doi.org/10.3390/cancers14102569
  • Statistics., B. C. (2023). How Common Is Breast Cancer? . Available online: https://www.cancer.org/cancer/breast-cancer/about/how-common-is-breast-cancer.html (accessed on 10 March 2023).
  • System., B. I. R. D. (2023). Available online: https://www.acr.org/Clinical-Resources/Reporting-and-DataSystems/Bi-Rads (accessed on 30 March 2023).
  • Taylor, C. R., Monga, N., Johnson, C., Hawley, J. R., & Patel, M. (2023). Artificial intelligence applications in breast imaging: current status and future directions. Diagnostics, 13(12), 2041.
  • Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGE handbook of qualitative research in psychology, 2(17-37), 25. https://doi.org/Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGE handbook of qualitative research in psychology, 2(17-37), 25.
  • Uzun Ozsahin, D., Ikechukwu Emegano, D., Uzun, B., & Ozsahin, I. (2022). The systematic review of artificial intelligence applications in breast cancer diagnosis. Diagnostics, 13(1), 45. https://doi.org/https://doi.org/10.3390/diagnostics13010045
  • Verma, H., Schaer, R., Reichenbach, J., Jreige, M., Prior, J. O., Evéquoz, F., & Depeursinge, A. (2021). On improving physicians’ trust in AI: Qualitative inquiry with imaging experts in the oncological domain. BMC Medical Imaging, in review.
  • https://doi.org/https://pdfs.semanticscholar.org/8497/a2a1b8c1ad9726c0bfbff7880408106d61ce.pdf W. Schulz, A. (2022). Tools of the trade: the bio-cultural evolution of the human propensity to trade. Biology & Philosophy, 37(2), 8.
  • Yagin, B., Yagin, F. H., Colak, C., Inceoglu, F., Kadry, S., & Kim, J. (2023). Cancer metastasis prediction and genomic biomarker identification through machine learning and eXplainable artificial intelligence in breast cancer research. Diagnostics, 13(21), 3314. https://doi.org/https://doi.org/10.3390/diagnostics13213314
  • Zhang J, W. J., Zhou XS, Shi F, Shen D. . (2024). Recent advancements in artificial intelligence for breast cancer:image augmentation, segmentation, diagnosis, and ijtos doi 10.22376/ijtos.2024.2.1.27-3634 prognosis approaches. Semin Cancer Biol. 2023 Sep 12;96:11-25. doi: 10.1016/j.semcancer.2023.09.001,PMID 37704183.
  • Žukauskas, P., Vveinhardt, J., & Andriukaitienė, R. (2018). Philosophy and paradigm of scientific research. Management culture and corporate social responsibility, 121(13), 506-518. https://doi.org/DOI: 10.5772/intechopen.70628
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Cerrahi Onkoloji
Bölüm Araştırma Makalesi
Yazarlar

Meltem İnce

Erken Görünüm Tarihi 19 Ağustos 2025
Yayımlanma Tarihi 30 Ağustos 2025
Gönderilme Tarihi 27 Haziran 2025
Kabul Tarihi 19 Ağustos 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 1

Kaynak Göster

APA İnce, M. (2025). BIOCULTURAL FACTORS INFLUENCING THE ADOPTION OF AI-POWERED BREAST IMAGING TOOLS: BIOLOGICAL PERSPECTIVES ON TECHNOLOGY IMPLEMENTATION IN CANCER CARE. International Anatolia Academic Online Journal Health Sciences, 11(1), 567-581.

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