Yapay Zekâ: Sağlık Hizmetlerinden Uygulamalar

Öz

Hizmet ve üretim sektöründe akıllı makinelerin sıklıkla kullanılmasıyla birlikte, son yıllarda hem Türkiye’de hem de dünyada tartışılmaya başlanan Yapay Zeka (YZ), insan gibi davranışlar sergileme, sayısal mantık yürütme, hareket, konuşma ve ses algılama gibi birçok yeteneğe sahip yazılımsal ve donanımsal sistemler bütünüdür. YZ aynı zamanda, doğadaki varlıkların akıllı davranışlarını yapay olarak ortaya çıkarmayı amaçlayan bir kuramı ifade etmektedir. YZ, yapay sinir ağları, makine öğrenme ve derin öğrenme gibi, çok sayıda teknik yoluyla sağlık hizmetlerinde de uygulanmaktadır. Bu bakımdan bu çalışmada, YZ uygulamalarının sağlık hizmetleri alanında oldukça faydalı sonuçlar ortaya çıkardığına yönelik birtakım mevcut kanıtları sunmak amaçlanmıştır. Bu doğrultuda, sağlık hizmetleri alanındaki YZ konulu makaleler ve uluslararası uygulama örnekleri incelenmiştir. Buna göre, YZ yöntem ve teknikleri sayesinde sağlık hizmetlerinde maliyetlerde ve oluşan kuyruklarda azalmalar olduğu ve teşhis ve tedavide ise daha kesin ve doğru sonuçlara ulaşıldığı görülmüştür. Bu açıdan, YZ kavramının sağlık hizmetleri alanında daha fazla yer alması sağlık hizmetlerinin sürdürülebilirliği ve kalitesi bakımından önemli görülmektedir.

Anahtar Kelimeler

• Akıllı makineler,
• Yapay zeka,
• Sağlık hizmetleri

Artificial Intelligence: Implementations From Healthcare Services

Öz

With the frequent use of smart machines in the service and production sector, Artificial Intelligence (AI) concept that in recent years both in Turkey and World have been begun to be discussed is a set of software and hardware systems which have many capabilities such as exhibiting behaviors like human, numerical reasoning, movement, speech and voice recognition. AI refers to a theory that aims to artificially reveal the intelligent behaviors of beings in nature. AI has also been implemented in healthcare services through numerous techniques such as artificial neural networks, machine learning and deep learning. Inthis context, in this study, it was aimed to provide some available evidence that have produced very beneficial results in the field of healthcare services. In this direction, the articles about AI and international implementation examples in the field of healthcare have been examined. Accordingly, it has been seen that there are decreases in costs and queues and more accurate and correct results in diagnosis and treatment in health care services thanks to AI methods and techniques. In this respect, the fact that the concept of AI is more involved in health care services have been considered important in terms of the sustainability and quality of health care services.

Anahtar Kelimeler

• Intelligent machines,
• Artificial intelligence,
• Healthcare servirces

Kaynakça

1. Agwu, O. E., Akpabio, J. U., Alabi, S. B. & Dosunmu, A. (2018). Artificial Intelligence Techniques and Their Applications in Drilling Fluid Engineering: A Review, Journal of Petroleum Science and Engineering, 167, 300-315.
2. 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, 1-35.
3. Amisha, P. M., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328-2331.
4. Anbar, A. (2002). Yeni Ekonomi Ve E-Ticaretin İstihdam-Çalışanlar Ve İnsan Kaynakları Üzerindeki Etkileri. ISGUC The Journal of Industrial Relations and Human Resources, 4(2).
5. Ashrafian, H., Darzi, A. & Athanasiou, T. (2015). A Novel Modification of the Turing Test for Artificial Intelligence and Robotics in Healthcare, The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS, 11(1), 38-43.
6. Atkinson, M. (1979). Artificial Intelligence and Natural Man: Margaret A. Boden, Philosophical Quarterly, 29(116), 278-281.
7. Bauer, M. S. (2002). A Review of Quantitative Studies of Adherence to Mental Health Clinical Practice Guidelines, Harvard Review of Psychiatry, 10(3), 138-153.
8. Bennett, C.C., & Hauser, K. (2013). Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach, Artificial Intelligence in Medicine, 57, 9-19.

9. Berlyand, Y., Raja, A. S., Dorner, S. C., Prabhakar, A. M., Sonis, J. D., Gottumukkala, R. V., ... & Yun, B. J. (2018). How artificial intelligence could transform emergency department operations. The American Journal of Emergency Medicine, 36(8), 1515-1517. https://doi.org/10.1016/j.ajem.2018.01.017.
10. Boesch, C. (2007). What Makes Us Human (Homo sapiens)? The Challenge of Cognitive Cross-Species Comparison, Journal of Comparative Psychology, 121(3), 227-240.
11. Chen, M. C., Ball, R. L., Yang, L., Moradzadeh, N., Chapman, B. E., Larson, D. B., ... & Lungren, M. P. (2017). Deep Learning to Classify Radiology Free-Text Reports. Radiology, 286(3), 845-852.
12. Contreras, I., & Vehi, J. (2018). Artificial Intelligence for Diabetes Management and Decision Support: Literature Review, Journal of medical Internet Research, 20(5), 1-21.
13. DeepMind Health. (2021). About DeepMind Health, https://deepmind.com/applied/deepmind-health/about-deepmind-health/. (Erişim Tarihi: 26.03.2021).
14. Eroğlu İ. (2010). Binalarda Enerji Yönetimi Ve Enerji Kullanım Verimliliğini Etkileyen Faktörlerin Yapay Zeka Teknikleri İle Analizi, Sakarya Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Sakarya.
15. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. (2017). Dermatologist-level Classification of Skin Cancer with Deep Neural Networks, Nature, 542, 115–8. https://doi.org/10.1038/nature21056.
16. Evans, J. (2018). How Apple’s AI Imaging Vision May Save Lives, Computerworld, August.
17. Genesereth, M. R. & Nilsson, N. J. (1988). Logical Foundations of Artificial Intelligence. Morgan Kaufmann Publishers.
18. Göksungur, A.E. (2008). Stok Kontrolünde Yapay Zeka Kavramı Ve Bir Uygulama, Marmara Üniversitesi Sosyal Bilimler Enstitüsü, Doktora Tezi, İstanbul.
19. Gurkaynak, G., Yilmaz, I., & Haksever, G. (2016). Stifling Artificial Intelligence: Human Perils, Computer Law & Security Review, 32(5), 749-758.
20. Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine, Metabolism-Clinical and Experimental, 69, 36-S40.
21. Hanson, C. W., & Marshall, B. E. (2001). Artificial Intelligence Applications in the Intensive Care Unit, Critical Care Medicine, 29(2), 427-435.
22. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature medicine, 25(1), 30-36.
23. IBM Watson Health. (2021). What is IBM Watson Health?, https://www.ibm.com/watson/health/about/. (Erişim Tarihi: 25.03.2021). Jackson, G. P., & Tarpley, J. L. (2009). How Long Does it Take to Train a Surgeon?, Bmj, 339, b4260.
24. Jarrahi, M. H. (2018). Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision Making, Business Horizons, 61(4), 577-586.
25. Jones, M.T. (2017). A Beginner's Guide to Artificial Intelligence, Machine Learning, And Cognitive Computing, USA: IBM Corporation.
26. Kalis, B., Collier, M. & Fu, R. (2018). Technology: 10 Promising AI Applications in HealthCare, Harv Bus Rev.May 2018. (Erişim Tarihi: 04.02.2019).
27. Kaplan, B. (2001). Evaluating Informatics Applications—Clinical Decision Support Systems Literature Review, International Journal of Medical Informatics, 64(1), 15-37.
28. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M. & Kitai, T. (2017). Artificial Intelligence in Precision Cardiovascular Medicine, Journal of the American College of Cardiology, 69(21), 2657-2664.
29. Krittanawong, C., Tunhasiriwet, A., Zhang, H., Wang, Z., Aydar, M. & Kitai, T. (2017). Deep Learning with Unsupervised Feature in Echocardiographic Imaging, Journal of the American College of Cardiology, 69(16), 2100-2101.
30. Lisboa, P. J. (2002). A Review of Evidence of Health Benefit from Artificial Neural Networks in Medical Intervention, Neural Networks, 15(1), 11-39.
31. McGlynn, E. A., Asch, S. M., Adams, J., Keesey, J., Hicks, J., DeCristofaro, A. & Kerr, E. A. (2003). The Quality of Health Care Delivered to Adults in the United States, New England Journal of Medicine, 348(26), 2635-2645.
32. Mekov, E., Miravitlles, M., & Petkov, R. (2020). Artificial intelligence and machine learning in respiratory medicine. Expert Review of Respiratory Medicine, 14(6), 559-564.
33. Lin, H. C., Tu, Y. F., Hwang, G. J., & Huang, H. (2021). From Precision Education to Precision Medicine. Educational Technology & Society, 24(1), 123-137.
34. Microsoft. (2016). Project Hanover, https://www.microsoft.com/en-us/research/project/project-hanover/. (Erişim Tarihi: 25.03.2021).
35. Nanayakkara, S., Fogarty, S., Ross, K., Milosevic, Z., Richards, B., Liew, D. & Kaye, D. (2018). Machine Learning Models Significant Improve Outcome Prediction After Cardiac Arrest, Journal of the American College of Cardiology, 71(11), A775.
36. Nilsson, N. J. (2014). Principles of Artificial Intelligence, Morgan Kaufmann.
37. Niu J., Tang W., Xu F., Zhou,X. & Song Y. (2016). Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis, ISPRS International Journal of Geo-Information, 5(5), 66.
38. Orszag, P. R. & Ellis, P. (2007). The Challenge of Rising Health Care Costs-A View From the Congressional Budget Office, New England Journal of Medicine, 357(18), 1793.
39. ProMed-mail. (2010). About ProMED-mail, https://promedmail.org/about-promed/. (Erişim Tarihi: 25.03.2021).
40. Ramesh, A. N., Kambhampati, C., Monson, J. R. & Drew, P. J. (2004). Artificial Intelligence in Medicine, Annals of the Royal College of Surgeons of England, 86(5), 334-338.
41. Rigla, M., García-Sáez, G., Pons, B., & Hernando, M. E. (2018). Artificial Intelligence Methodologies and Their Application to Diabetes, Journal of Diabetes Science and Technology, 12(2), 303-310.
42. Rundle, C. W., Hollingsworth, P., & Dellavalle, R. P. (2021). Artificial intelligence in dermatology for the public. Clinics in Dermatology.
43. Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, et al. (2017). Natural and Artificial Intelligence in Neurosurgery: A Systemic Review, Neurosurgery, 0, 1–12. https://doi.org/10.1093/neuros/nyx384.
44. Shi, Z. Z., & Zheng, N. N. (2006). Progress and Challenge of Artificial Intelligence, Journal of Computer Science and Technology, 21(5), 810-822.
45. Tajik, A.J. (2016). Machine Learning for Echocardiographic Imaging: Embarking on Another Incredible Journey, Journal of the American College of Cardiology, 68(21), s.2296-2298.
46. World Economic Forum. (2016). The Global Competitiveness Report 2016–2017, World Geneva: Economic Forum.