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Concept of Artificial Intelligence-oriented Public Health Model in Cancer Care

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28 set 2024
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World Health Organization. (n.d.). Cancer. World Health Organization. https://www.who.int/health-topics/cancer World Health Organization (n.d.) Cancer World Health Organization https://www.who.int/health-topics/cancer Search in Google Scholar

Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., ... & Ledsam, J. R. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271–e297. LiuX. FaesL. KaleA. U. WagnerS. K. FuD. J. BruynseelsA. LedsamJ. R. 2019 A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis The Lancet Digital Health 1 6 e271 e297 Search in Google Scholar

Tan, D. S., Mok, T. S., & Rebbeck, T. R. (2016). Cancer genomics: diversity and disparity across ethnicity and geography. J Clin Oncol, 34(1), 91–101. TanD. S. MokT. S. RebbeckT. R. 2016 Cancer genomics: diversity and disparity across ethnicity and geography J Clin Oncol 34 1 91 101 Search in Google Scholar

Ung, K. A., Campbell, B. A., Duplan, D., Ball, D., & David, S. (2016). Impact of the lung oncology multidisciplinary team meetings on the management of patients with cancer. Asia-Pacific Journal of Clinical Oncology, 12(2), e298–e304. UngK. A. CampbellB. A. DuplanD. BallD. DavidS. 2016 Impact of the lung oncology multidisciplinary team meetings on the management of patients with cancer Asia-Pacific Journal of Clinical Oncology 12 2 e298 e304 Search in Google Scholar

Stewart, B. W. K. P., & Wild, C. P. (2019). World cancer report 2014. Public Health. StewartB. W. K. P. WildC. P. 2019 World cancer report 2014 Public Health Search in Google Scholar

Marr B. (2018, May 21). How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Retrieved from https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#49a4c84a60ba MarrB. 2018 May 21 How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#49a4c84a60ba Search in Google Scholar

Corish B. (2018, April 23). Medical knowledge doubles every few months; how can clinicians keep up? Retrieved from https://www.elsevier.com/connect/medical-knowledge-doubles-every-few-months-how-can-clinicians-keep-up CorishB. 2018 April 23 Medical knowledge doubles every few months; how can clinicians keep up? Retrieved from https://www.elsevier.com/connect/medical-knowledge-doubles-every-few-months-how-can-clinicians-keep-up Search in Google Scholar

Mathew, A. (2018). Global survey of clinical oncology workforce. Journal of global oncology, 4, 1–12. MathewA. 2018 Global survey of clinical oncology workforce Journal of global oncology 4 1 12 Search in Google Scholar

ESMO (2019). ESMO Clinical Practice Guidelines. Retrieved from https://www.esmo.org/Guidelines ESMO 2019 ESMO Clinical Practice Guidelines Retrieved from https://www.esmo.org/Guidelines Search in Google Scholar

NCCN (2019). NCCN Guidelines® & Clinical Resources. Retrieved from https://www.nccn.org/professionals/physician_gls/default.aspx NCCN 2019 NCCN Guidelines® & Clinical Resources Retrieved from https://www.nccn.org/professionals/physician_gls/default.aspx Search in Google Scholar

Begun, J. W., & Thygeson, M. (2015). Managing complex healthcare organizations. Handbook of healthcare management. Northampton: Edward Elgar, 1–17. BegunJ. W. ThygesonM. 2015 Managing complex healthcare organizations. Handbook of healthcare management Northampton Edward Elgar 1 17 Search in Google Scholar

Loftus, G. R., & Loftus, E. F. (2019). Human memory: The processing of information. LoftusG. R. LoftusE. F. 2019 Human memory: The processing of information Search in Google Scholar

Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., ... & Sanli, K. (2017). A pathology atlas of the human cancer transcriptome. Science, 357(6352), eaan2507. UhlenM. ZhangC. LeeS. SjöstedtE. FagerbergL. BidkhoriG. SanliK. 2017 A pathology atlas of the human cancer transcriptome Science 357 6352 eaan2507 Search in Google Scholar

Altshuler, Y., Pentland, A., & Bruckstein, A. M. (2018). The cooperative hunters–efficient and scalable drones swarm for multiple targets detection. Swarms and Network Intelligence in Search, 187–205. AltshulerY. PentlandA. BrucksteinA. M. 2018 The cooperative hunters–efficient and scalable drones swarm for multiple targets detection Swarms and Network Intelligence in Search 187 205 Search in Google Scholar

Flin, R., Salas, E., Straub, M., & Martin, L. (2017). Decision-making under stress: Emerging themes and applications. FlinR. SalasE. StraubM. MartinL. 2017 Decision-making under stress: Emerging themes and applications Search in Google Scholar

Gibbs S. (2017, August 20). Elon Musk leads 116 experts calling for outright ban of killer robots. Retrived from https://www.theguardian.com/technology/2017/aug/20/elon-musk-killer-robots-experts-outright-ban-lethal-autonomous-weapons-war GibbsS. 2017 August 20 Elon Musk leads 116 experts calling for outright ban of killer robots Retrived from https://www.theguardian.com/technology/2017/aug/20/elon-musk-killer-robots-experts-outright-ban-lethal-autonomous-weapons-war Search in Google Scholar

Kontzer T. (2016, September 19). Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85%. Retrived from https://blogs.nvidia.com/blog/2016/09/19/deep-learning-breast-cancer-diagnosis KontzerT. 2016 September 19 Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85% Retrived from https://blogs.nvidia.com/blog/2016/09/19/deep-learning-breast-cancer-diagnosis Search in Google Scholar

Behold AI (2019). Pioneering artificial intelligence in healthcare. Retrived from https://behold.ai/howit-works/ Behold AI 2019 Pioneering artificial intelligence in healthcare Retrived from https://behold.ai/howit-works/ Search in Google Scholar

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115. EstevaA. KuprelB. NovoaR. A. KoJ. SwetterS. M. BlauH. M. ThrunS. 2017 Dermatologist-level classification of skin cancer with deep neural networks Nature 542 7639 115 Search in Google Scholar

Case Western Reserve University (2019, September 21) Computer program beats physicians at brain cancer diagnoses. Retrived from https://thedaily.case.edu/computer-program-beats-physicians-brain-cancer-diagnoses/ Case Western Reserve University 2019 September 21 Computer program beats physicians at brain cancer diagnoses Retrived from https://thedaily.case.edu/computer-program-beats-physicians-brain-cancer-diagnoses/ Search in Google Scholar

Xiao, Z., Huang, R., Ding, Y., Lan, T., Dong, R., Qin, Z., ... & Wang, W. (2016, October). A deep learning-based segmentation method for brain tumor in MR images. In 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (pp. 1–6). IEEE. XiaoZ. HuangR. DingY. LanT. DongR. QinZ. WangW. 2016 October A deep learning-based segmentation method for brain tumor in MR images In 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) 1 6 IEEE Search in Google Scholar

Zhang, Q., Xiao, Y., Suo, J., Shi, J., Yu, J., Guo, Y., ... & Zheng, H. (2017). Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography. Ultrasound in medicine & biology, 43(5), 1058–1069. ZhangQ. XiaoY. SuoJ. ShiJ. YuJ. GuoY. ZhengH. 2017 Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography Ultrasound in medicine & biology 43 5 1058 1069 Search in Google Scholar

Jäger, P. F., Bickelhaupt, S., Laun, F. B., Lederer, W., Heidi, D., Kuder, T. A., ... & Schlemmer, H. P. (2017, September). Revealing hidden potentials of the q-Space signal in breast cancer. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 664–671). Springer, Cham. JägerP. F. BickelhauptS. LaunF. B. LedererW. HeidiD. KuderT. A. SchlemmerH. P. 2017 September Revealing hidden potentials of the q-Space signal in breast cancer In International Conference on Medical Image Computing and Computer-Assisted Intervention 664 671 Springer, Cham Search in Google Scholar

Stumpe M., Peng L. (2017, March 3). Assisting Pathologists in Detecting Cancer with Deep Learning. Retrived from https://ai.googleblog.com/2017/03/assisting-pathologists-in-detecting.html StumpeM. PengL. 2017 March 3 Assisting Pathologists in Detecting Cancer with Deep Learning Retrived from https://ai.googleblog.com/2017/03/assisting-pathologists-in-detecting.html Search in Google Scholar

Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., ... & Van Der Laak, J. (2016). Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Scientific reports, 6, 26286. LitjensG. SánchezC. I. TimofeevaN. HermsenM. NagtegaalI. KovacsI. Van Der LaakJ. 2016 Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis Scientific reports 6 26286 Search in Google Scholar

Hyung, W. J., Son, T., Park, M., Lee, H., Kim, Y. N., Kim, H. I., ... & Kim, J. (2017). Superior prognosis prediction performance of deep learning for gastric cancer compared to Yonsei prognosis prediction model using Cox regression. Journal of Clinical Oncology, 35(4_suppl), 164–164. HyungW. J. SonT. ParkM. LeeH. KimY. N. KimH. I. KimJ. 2017 Superior prognosis prediction performance of deep learning for gastric cancer compared to Yonsei prognosis prediction model using Cox regression Journal of Clinical Oncology 35 4_suppl 164 164 Search in Google Scholar

Turkki, R., Linder, N., Kovanen, P. E., Pellinen, T., & Lundin, J. (2016). Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. Journal of pathology informatics, 7. TurkkiR. LinderN. KovanenP. E. PellinenT. LundinJ. 2016 Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples Journal of pathology informatics 7 Search in Google Scholar

Danaee, P., Ghaeini, R., & Hendrix, D. A. (2017). A deep learning approach for cancer detection and relevant gene identification. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017 (pp. 219–229). DanaeeP. GhaeiniR. HendrixD. A. 2017 A deep learning approach for cancer detection and relevant gene identification In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017 219 229 Search in Google Scholar

Yuan, Y., Shi, Y., Li, C., Kim, J., Cai, W., Han, Z., & Feng, D. D. (2016). DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations. BMC bioinformatics, 17(17), 476. YuanY. ShiY. LiC. KimJ. CaiW. HanZ. FengD. D. 2016 DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations BMC bioinformatics 17 17 476 Search in Google Scholar

Driessen H. (2017, March 6). Philips and LabPON plan to create world’s largest pathology database of annotated tissue images for deep learning. Retrived from https://www.philips.com/a-w/about/news/archive/standard/news/press/2017/20170306-philips-and-labpon-plan-to-create-worlds-largest-pathology-database-of-annotated-tissue-images-for-deep-learning.html DriessenH. 2017 March 6 Philips and LabPON plan to create world’s largest pathology database of annotated tissue images for deep learning Retrived from https://www.philips.com/a-w/about/news/archive/standard/news/press/2017/20170306-philips-and-labpon-plan-to-create-worlds-largest-pathology-database-of-annotated-tissue-images-for-deep-learning.html Search in Google Scholar

Oak Ridge National Laboratory (2016, November 14) Accelerating Cancer Research With Deep Learning. Retrived from https://cacm.acm.org/news/209708-accelerating-cancer-research-with-deep-learning/fulltext Oak Ridge National Laboratory 2016 November 14 Accelerating Cancer Research With Deep Learning Retrived from https://cacm.acm.org/news/209708-accelerating-cancer-research-with-deep-learning/fulltext Search in Google Scholar

Ross, C., & Swetlitz, I. (2018). IBM’s Watson supercomputer recommended ‘unsafe and incorrect’cancer treatments, internal documents show. Stat News https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafeincorrect-treatments. RossC. SwetlitzI. 2018 IBM’s Watson supercomputer recommended ‘unsafe and incorrect’cancer treatments, internal documents show Stat News https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafeincorrect-treatments. Search in Google Scholar

FDA (2019, May 11). Artificial Intelligence and Machine Learning in Software as a Medical Device. Retrived from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device FDA 2019 May 11 Artificial Intelligence and Machine Learning in Software as a Medical Device Retrived from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device Search in Google Scholar

Azati Software (2019, January 23). How much does Artificial Intelligence (AI) cost in 2019? Retrived from https://azati.ai/how-much-does-it-cost-to-utilize-machine-learning-artificial-intelligence/ Azati Software 2019 January 23 How much does Artificial Intelligence (AI) cost in 2019? Retrived from https://azati.ai/how-much-does-it-cost-to-utilize-machine-learning-artificial-intelligence/ Search in Google Scholar

Lingua:
Inglese
Frequenza di pubblicazione:
2 volte all'anno
Argomenti della rivista:
Medicina, Medicina clinica, Medicina interna, Ematologia, Oncologia