Tanı ve Tedavide Yapay Zeka

Year 2024, Volume: 5 Issue: 2, 107 - 118, 14.06.2024

Mustafa Öztatlıcı , Seçil Eroğlu , Hülya Öztatlıcı , Mehmet Göl

https://doi.org/10.46871/eams.1470170

Abstract

Yapay zeka (YZ), bilgisayar bilimi içerisinde geniş uygulamalara sahip ve tıbbi teknolojileri dönüştüren bir alandır. Genellikle karmaşık problemleri minimal teori ve birçok uygulamayla çözebilen bilgisayar bilimi dalı olarak kabul edilir. YZ, araştırmacılara büyük veri setlerinin analizinde yardımcı olmak, hassas tıbbı mümkün kılmak ve hekimlerin hasta sonuçlarını anlamlandırmada yardımcı olmak için kullanılmaktadır. YZ'deki yeni teknikler, multiomik veri kümelerinden elde edilen yeni bilgileri anlamlandırmak için çeşitli veri türlerini bir araya getirebilir. Yapay zekanın bir alt kümesi olan makine öğrenimi ile birlikte yüksek kaliteli verilerin analiz edilmesi, hastaların sağlıksız davranışlarını değiştirmeye, cerrahi ve küratif bir tedaviden sonra kronik hastalıkların riskini veya nüksünü tahmin etmeye, kronik hastalıkları olan hastaların ilerleme ve hayatta kalma oranlarını tahmin etmeye, terapötik ihtiyaca, gelişmiş klinik çalışma yorumlarının oluşturulmasına ve yeni hedeflerin belirlenmesine yardımcı olabilir. Bununla birlikte, hassas tıbbın sağlık hizmetlerinde etkin bir şekilde uygulanabilmesi için daha kullanıcı dostu bir arayüze ihtiyaç duyulacaktır. Yapay zeka teknolojileri, insan zekasıyla yakın işbirliği içinde doğru, adil ve sağlam bir şekilde uygulanırsa, dünya çapında etkili ve kişiselleştirilmiş sağlık hizmetleri için yeni olanaklar yaratması beklenmektedir. Bu derlemede, yapay zeka teknolojisinin genel hatları, sağlık hizmetlerinde uygulama alanları ve geleceği gözden geçirilmektedir.

Keywords

Yapay Zeka, Açıklanabilir Yapay Zeka, Makine Öğrenimi, Tıp, İlaç Keşfi

Artificial Intelligence in Diagnosis and Treatment

Abstract

Artificial intelligence (AI) is a field within computer science that has vast applications and has transformed medical technologies. It is often regarded to be the branch of computer science that can handle complicated problems with minimal theory and many applications. AI is utilized to assist researchers in the analysis of large data sets, enabling precision medicine and assisting physicians in improving patient outcomes. New techniques in AI can bring together various types of data to make sense of new information obtained from multiomics datasets. Analyzing high-quality data combined with machine learning, a subset of AI, can help modify patients' unhealthy behaviors, predict risk or recurrence of chronic diseases after a surgical and curative treatment, prediction of progression and survival rates of patients with chronic diseases, therapeutic need, generation of improved clinical trial interpretations and identification of new targets. Howeveri, to effectively implement precision medicine in healthcare, a more user-friendly interface would be required. If AI technologies are applied correctly, fairly and robustly, in close cooperation with human intelligence, it is expected to open up new possibilities for effective and personalised healthcare services worldwide. In this review, the general outlines of AI technology, its application areas in healthcare and its future are overviewed.

Keywords

Artificial Intelligence, Explainable Artificial Intelligence, Machine Learning, Medicine, Drug Discovery

Supporting Institution

-

References

• 1. Amann J, Blasimme A, Vayena E, et al. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20:310. https://doi.org/10.1186/s12911-020-01332-6.
• 2. Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med. 2020;7:27. doi: 10.3389/fmed.2020.00027.
• 3. Hulsen T. Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare. AI. 2023;4:652-666. https://doi.org/10.3390/ai4030034.
• 4. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689. https://doi.org/10.1186/s12909-023-04698-z.
• 5. Dragoni M, Donadello I, Eccher C. Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice. Artif Intell Med. 2020;105:101840. doi: 10.1016/j.artmed.2020.101840.
• 6. Lou SJ, Hou MF, Chang HT, Chiu CC, Lee HH, Yeh SJ, et al. Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study. Cancers. 2020;12(12):3817. doi: 10.3390/cancers12123817. 7. Ferroni P, Zanzotto FM, Riondino S, Scarpato N, Guadagni F, Roselli M. Breast Cancer Prognosis Using a Machine Learning Approach. Cancers. 2020;11(13):328. doi: 10.3390/cancers11030328.
• 8. Greshock J, Lewi M, Hartog B, Tendler C. Harnessing Real-World Evidence for the Development of Novel Cancer Therapies. Trends Cancer. 2020;6(11):907-909. doi: 10.1016/j.trecan.2020.08.006.
• 9. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8-17. https://doi.org/10.1016/j.csbj.2014.11.005.
• 10. Kawakami E, Tabata J, Yanaihara N, Ishikawa T, Koseki K, Iida Y, et al. Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers. Clin Cancer Res. 2019;25(10):3006-3015. doi: 10.1158/1078-0432.CCR-18-3378.
• 11. Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-Generation Machine Learning for Biological Networks. Cell. 2018;173(7):1581-1592. doi: 10.1016/j.cell.2018.05.015.
• 12. Li J, Chen H, Wang Y, Chen MM, Liang H. Next-Generation Analytics for Omics Data. Cancer Cell. 2021;39(1):3-6. doi: 10.1016/j.ccell.2020.09.002.
• 13. Hutter C, Zenklusen JC. The Cancer Genome Atlas: Creating Lasting Value beyond Its Data. Cell. 2018;173(2):283-285. doi: 10.1016/j.cell.2018.03.042.
• 14. Srivastava S, Ghosh S, Kagan J, Mazurchuk R, National Cancer Institute’s, HI. The Making of a PreCancer Atlas: Promises, Challenges, and Opportunities. Trends Cancer. 2018;4(8):523-536. doi: 10.1016/j.trecan.2018.06.007.
• 15. Chakraborty D, Ivan C, Amero P, Khan M, Rodriguez-Aguayo C, Başağaoğlu H, et al. Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer. Cancers. 2021;13(14):3450. https://doi.org/10.3390/cancers13143450.
• 16. Xu F, Uszkoreit H, Du Y, Fan W, Zhao D, Zhu J. Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. In: Tang J, Kan MY, Zhao D, Li S, Zan H, editors. Natural Language Processing and Chinese Computing, Lecture Notes in Computer Science. Springer, Cham. 2019;11839. https://doi.org/10.1007/978-3-030-32236-6_51.
• 17. Zhang Y, Weng Y, Lund J. Applications of Explainable Artificial Intelligence in Diagnosis and Surgery. Diagnostics. 2022;12(2):237. doi: 10.3390/diagnostics12020237.
• 18. Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang GZ. XAI-Explainable artificial intelligence. Sci Robot. 2019;4(37). doi: 10.1126/scirobotics.aay7120.
• 19. Sönmez PK, Turhan A, Öztatlıcı M, Özbilgin K. Effects of Verteporfin-mediated photodynamic therapy in breast cancer cells. Indian J Biochem Biophys. 2020;57(5):560-566. https://doi.org/10.56042/ijbb.v57i5.30012.
• 20. Amoroso N, Pomarico D, Fanizzi A, Didonna V, Giotta F, La Forgia D, et al. A roadmap towards breast cancer therapies supported by explainable artificial intelligence. Appl Sci. 2021;11(11):4881. doi: 10.3390/app11114881.
• 21. Turek M. DARPA - Explainable Artificial Intelligence (XAI) Program. 2017. https://www.darpa.mil/program/explainable-artificial-intelligence (Accessed: 23.08.2022).
• 22. Farahani N, Parwani AV, Pantanowitz L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol Lab Med Int. 2015;7:23-33.
• 23. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5). doi: 10.1016/S1470-2045(19)30154-8.
• 24. Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288:62-81. https://doi.org/10.1111/joim.13030.
• 25. Mukhopadhyay S, Feldman MD, Abels E, et al. Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (Pivotal Study). Am J Surg Pathol. 2017;42:39-52.
• 26. Zarella MD, Bowman D, Aeffner F, Farahani N, Xthona A, Absar SF, Parwani A, Bui M, Hartman DJ. A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association. Arch Pathol Lab Med. 2018.
• 27. Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol. 2023 Oct 3;18(1):109. doi: 10.1186/s13000-023-01375-z. PMID: 37784122; PMCID: PMC10546747.
• 28. Wang X, et al. Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer. American Society of Clinical Oncology; 2018.
• 29. Saltz J, et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 2018;23(1):181–93. doi: 10.1016/j.celrep.2018.03.086.
• 30. Yuan Y. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. J R Soc Interface. 2015;12(103):20141153. doi: 10.1098/rsif.2014.1153.
• 31. Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev. 2023;56(7):5975-6037. doi: 10.1007/s10462-022-10306-1.
• 32. Pantic I, Paunovic J, Pejic S, Drakulic D, Todorovic A, Stankovic S, Vucevic D, Cumic J, Radosavljevic T. Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art. Chem Biol Interact. 2022 May 1;358:109888.
• 33. Undru TR, Uday U, Lakshmi JT, Kaliappan A, Mallamgunta S, Nikhat SS, Sakthivadivel V, Gaur A. Integrating Artificial Intelligence for Clinical and Laboratory Diagnosis - a Review. Maedica (Bucur). 2022 Jun;17(2):420-426. doi: 10.26574/maedica.2022.17.2.420.
• 34. MacEachern SJ, Forkert ND. Machine learning for precision medicine. Genome. 2021;64(4):416-425.
• 35. Tabur S, et al. Association of Rho/Rho-kinase gene polymorphisms and expressions with obesity-related metabolic syndrome. Eur Rev Med Pharmacol Sci. 2015;19(9):1680-1688.
• 36. Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983-987.
• 37. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning–based sequence model. Nat Methods. 2015;12(10):931-934.
• 38. Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33(8):831-838.
• 39. Yin B, Balvert M, van der Spek RAA, Dutilh BE, et al. Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. Bioinformatics. 2019;35(14).
• 40. Singh R, Lanchantin J, Robins G, Qi Y. DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics. 2016;32(17).
• 41. Kalinin AA, et al. Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics. 2018;19(7):629-650.
• 42. Fisher AJ, Medaglia JD, Jeronimus BF. Lack of group-to-individual generalizability is a threat to human subjects research. Proc Natl Acad Sci U S A. 2018;115(27).
• 43. Food and Drug Administration. Clinical pharmacogenomics: premarket evaluation in early-phase clinical studies and recommendations for labeling. US Department of Health and Human Services, Silver Spring, MD, USA. 2013; www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM337169.pdf.
• 44. Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016;6:26094.
• 45. Pham T, et al. Deepcare: A deep dynamic memory model for predictive medicine. Adv Knowl Discov Data Min. 2016;2:3-14.
• 46. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583-589.
• 47. Ramsundar B. Molecular machine learning with DeepChem [PhD thesis]. Stanford University; 2018.
• 48. Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33(8):831-838.
• 49. Qureshi R, Irfan M, Gondal TM, et al. AI in drug discovery and its clinical relevance. Heliyon. 2023;9.