Kişiye Özel Tıbbi Tedavi: Yapay Zeka ile Sağlık Hizmetlerinde Yeni Bir Dönem
Özet
Kişiselleştirilmiş tıp, bireyin genetik, epigenetik, çevresel ve yaşam tarzı faktörlerini bütünsel değerlendiren ve tedavileri bu bilgilerle optimize eden yeni bir yaklaşım olup, yapay zekanın büyük veri analiziyle hastalıkların erken teşhisi, tanı ve tedavi süreçlerini önemli ölçüde hızlandırmakta ve başarıyı artırmaktadır. Yapay zeka, özellikle kanser, diyabet ve kalp hastalıklarında klinik karar destek sistemleri aracılığıyla hastaya özel tedavi protokolleri sunarken, genomik ve biyobelirteç analizleri de teşhis ve tedaviyi daha kesin hale getiriyor. Ancak veri gizliliği, algoritmik önyargılar ve düzenleyici zorluklar gibi engellerle karşılaşan bu teknolojilerin, gelecekte mahremiyet ve şeffaflığı artıracak federatif öğrenme ve açıklanabilir yapay zeka gibi çözümlerle desteklenmesi ve küresel iş birlikleriyle erişim eşitliğinin sağlanması büyük önem taşıyor. Sonuç olarak, yapay zekanın kişiselleştirilmiş tıp alanında sunduğu büyük potansiyel, etik ve politik düzenlemelerle uyumlu şekilde, sağlık hizmetlerini daha etkili, erişilebilir ve hasta odaklı hale getirmenin anahtarıdır.
Personalized medicine is a novel approach that evaluates an individual's genetic, epigenetic, environmental, and lifestyle factors holistically. It utilizes this information, along with artificial intelligence-driven big data analysis, to significantly accelerate early diagnosis, accurate diagnosis, and treatment processes, thereby increasing success rates. Artificial intelligence provides patient-specific treatment protocols through clinical decision support systems, especially in the fields of cancer, diabetes, and cardiovascular diseases. Additionally, genomic and biomarker analyses enhance the precision of diagnosis and treatment. However, these technologies face challenges such as data privacy concerns, algorithmic biases, and regulatory hurdles. Solutions like federated learning and explainable artificial intelligence are being developed to improve privacy and transparency. Ensuring equal access through global collaboration is of great importance. Ultimately, the immense potential of artificial intelligence in personalized medicine holds the key to making healthcare more effective, accessible, and patient-centered, while adhering to ethical and regulatory standards.
Referanslar
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Chen, H., ve ark. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250.
Collins, F. S., & Varmus, H. (2015). A new initiative on precisionmedicine. New England Journal of Medicine, 372(9), 793-795.
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., ... & Tsirigos, A. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559-1567.
Dagdeviren, C., ve ark. (2014). Conformal piezoelectric energy harvesting and storage from motions of the heart, lung, and diaphragm. Proceedings of the National Academy of Sciences, 111(27), 10153–10158.
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Kolachalama, V. B., & Garg, P. S. (2018). Machine learning and medical education. NPJ Digital Medicine, 1(1), 54.
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Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332.
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Lloyd-Price, J., ve ark. (2016). The healthy human microbiome. Genome Medicine, 8(1), 51.
Luzum, J. A., ve ark. (2017). The Pharmacogenomics Research Network Translational Pharmacogenetics Program: outcomes and metrics of pharmacogenetic implementations across diverse healthcare systems. Clinical Pharmacology & Therapeutics, 102(3), 502-510.
Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 24(3), 773-780.
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P., & Hirschhorn, J. N. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics, 9(5), 356-369.
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2016). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., ve ark. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392(10157), 1553–1598.
Price, W. N., Gerke, S., & Cohen, I. G. (2019). Potential Liability for Physicians Using Artificial Intelligence. JAMA, 322(18), 1765–1766.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
Rajkomar, A., ve ark. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38
Relling, M. V., & Evans, W. E. (2015). Pharmacogenomics in the clinic. Nature, 526(7573), 343-50.
Shabani, M., & Marelli, L. (2021). Re-identifiability of genomic data and the GDPR. EMBO Reports, 22(3), e52445.
Shademan, A., Decker, R. S., Opfermann, J. D., Leonard, S., Krieger, A., & Kim, P. C. (2016). Supervised autonomous robotic soft tissue surgery. Science Translational Medicine, 8(337), 337ra64.
Shickel, B., ve ark. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604.
Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical Decision Support in the Era of Artificial Intelligence. JAMA, 320(21), 2199-2200.
Shuldiner, A. R., ve ark. (2013). The Pharmacogenomics Research Network Translational Pharmacogenetics Program: overcoming challenges of real-world implementation. Clinical Pharmacology & Therapeutics, 94(2), 207–210.
Slamon, D. J., ve ark. (2001). Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. New England Journal of Medicine, 344(11), 783-792.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
Torous, J., ve ark. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318–335.
Wan, J. C. M., ve ark. (2017). Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nature Reviews Cancer, 17(4), 223-238.
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., ... & Aspuru-Guzik, A. (2020). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(10), 1166-1172.
Zmora, N., ve ark. (2018). Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell, 174(6), 1388-1405.
Referanslar
Abreu, P. H., ve ark. (2016). Predicting breast cancer recurrence using machine learning techniques: A systematic review. ACM Computing Surveys (CSUR), 49(3), Article 52, 1–40.
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310.
Ardila, D., ve ark. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25, 954–961.
Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507-22.
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — Addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983.
Chen, H., ve ark. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250.
Collins, F. S., & Varmus, H. (2015). A new initiative on precisionmedicine. New England Journal of Medicine, 372(9), 793-795.
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., ... & Tsirigos, A. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559-1567.
Dagdeviren, C., ve ark. (2014). Conformal piezoelectric energy harvesting and storage from motions of the heart, lung, and diaphragm. Proceedings of the National Academy of Sciences, 111(27), 10153–10158.
Doshi-Velez, F., & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608.
EMA (European Medicines Agency). (2020). Reflection paper on the use of artificial intelligence in the lifecycle of medicines.
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), 115-118.
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G. S., Thrun, S., & Dean, J. (2021). A guide to deep learning in healthcare. Nature Medicine, 27(1), 14–18.
European Commission. (2021). Proposal for a Regulation laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act).
Feinberg, A. P. (2018). The key role of epigenetics in human disease prevention and mitigation. New England Journal of Medicine, 378(14), 1323-1334.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689-707.
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016). Coming of age: ten years of next-generation sequencing technologies. Nature Reviews Genetics, 17(6), 333-351.
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.
Henry, N. L., & Hayes, D. F. (2012). Cancer biomarkers. Molecular Oncology, 6(2), 140-146.
Herron, D. M., & Marohn, M. (2008). A consensus document on robotic surgery. Surgical Endoscopy, 22(2), 313–325.
Jameson, J. L., & Longo, D. L. (2015). Precision medicine—personalized, problematic, and promising. New England Journal of Medicine, 372(23), 2229-2234.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4).
Johnson, J. A., ve ark. (2017). Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for pharmacogenetics-guided warfarin dosing. Clinical Pharmacology & Therapeutics, 102(3), 397-404.
Kolachalama, V. B., & Garg, P. S. (2018). Machine learning and medical education. NPJ Digital Medicine, 1(1), 54.
Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716-1720
Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Lloyd-Price, J., ve ark. (2016). The healthy human microbiome. Genome Medicine, 8(1), 51.
Luzum, J. A., ve ark. (2017). The Pharmacogenomics Research Network Translational Pharmacogenetics Program: outcomes and metrics of pharmacogenetic implementations across diverse healthcare systems. Clinical Pharmacology & Therapeutics, 102(3), 502-510.
Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 24(3), 773-780.
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P., & Hirschhorn, J. N. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics, 9(5), 356-369.
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2016). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., ve ark. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392(10157), 1553–1598.
Price, W. N., Gerke, S., & Cohen, I. G. (2019). Potential Liability for Physicians Using Artificial Intelligence. JAMA, 322(18), 1765–1766.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
Rajkomar, A., ve ark. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38
Relling, M. V., & Evans, W. E. (2015). Pharmacogenomics in the clinic. Nature, 526(7573), 343-50.
Shabani, M., & Marelli, L. (2021). Re-identifiability of genomic data and the GDPR. EMBO Reports, 22(3), e52445.
Shademan, A., Decker, R. S., Opfermann, J. D., Leonard, S., Krieger, A., & Kim, P. C. (2016). Supervised autonomous robotic soft tissue surgery. Science Translational Medicine, 8(337), 337ra64.
Shickel, B., ve ark. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604.
Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical Decision Support in the Era of Artificial Intelligence. JAMA, 320(21), 2199-2200.
Shuldiner, A. R., ve ark. (2013). The Pharmacogenomics Research Network Translational Pharmacogenetics Program: overcoming challenges of real-world implementation. Clinical Pharmacology & Therapeutics, 94(2), 207–210.
Slamon, D. J., ve ark. (2001). Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. New England Journal of Medicine, 344(11), 783-792.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
Torous, J., ve ark. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318–335.
Wan, J. C. M., ve ark. (2017). Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nature Reviews Cancer, 17(4), 223-238.
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., ... & Aspuru-Guzik, A. (2020). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(10), 1166-1172.
Zmora, N., ve ark. (2018). Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell, 174(6), 1388-1405.
