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TIPTA YAPAY ZEKA UYGULAMALARI

Year 2022, , 604 - 613, 31.12.2022
https://doi.org/10.24938/kutfd.1214512

Abstract

Yapay Zeka (YZ), bir makine yardımıyla muhakeme, öğrenme, sınıflandırma ve yaratıcılık gibi insani beceriler sergileyen bir dizi algoritmalar bütünüdür. Bu YZ algoritmaları, derin öğrenme ve makine öğrenimi yoluyla insan zekasını taklit etmeye çalışır. Sağlık sektöründeki verilerin artışı ve ulaşılabilirliği, son zamanlardaki başarılı YZ uygulamalarını mümkün kılmıştır. YZ teknolojisi, karmaşık ve büyük verilerin altında saklanan klinik bilgileri su üstüne çıkararak, doktorların yargı ve karar mekanizmalarında büyük fayda sağlayabilir. Geniş klinik kullanımı henüz sınırlı olsa da araştırmalar, YZ'nın hastalıkların teşhisi, tedavisi, izlenmesi, sınıflandırılması ve risk taşıyan durumların ayırt edilmesinde başarıyla kullanılabileceğini göstermektedir. YZ'nın gelecekte doktorların yerini alabileceği düşünülmese de insan yargısının yerini alacağı öngörülmektedir. Bu derlemede, yapay zeka teknolojisinin genel hatları, sağlık hizmetlerinde uygulama alanları, geleceği ve muhtemel etik sorunlar gözden geçirilmektedir.

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References

  • Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021;41(6):1105-15.
  • Crossnohere NL, Elsaid M, Paskett J, Bose-Brill S, Bridges JFP. Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks. J Med Internet Res. 2022;24(8):e36823.
  • Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019;111(9):916-22.
  • Gong J, Liu JY, Sun XW, Zheng B, Nie SD. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules. Phys Med Biol. 2018;63(3):35036.
  • Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-75.
  • Sakagianni A, Feretzakis G, Kalles D, Koufopoulou C, Kaldis V. Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans. Stud Health Technol Inform. 2020;272:13-6.
  • Stoel BC. Artificial intelligence in detecting early RA. Semin Arthritis Rheum. 2019;49(3S):S25-S28.
  • Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. J Clin Med. 2019;8(11):1976.
  • Hwang Y, Lee HH, Park C, Tama BA, Kim JS, Cheung DY et al. Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network. Dig Endosc. 2021;33(4):598-607.
  • Hart SN, Flotte W, Norgan AP, Shah KK, Buchan ZR, Mounajjed T et al. Classification of Melanocytic Lesions in Selected and Whole-Slide Images via Convolutional Neural Networks. J Pathol Inform. 2019;10:5.
  • Kosaraju SC, Hao J, Koh HM, Kang M. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods. 2020;179:3-13.
  • Navarrete AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020;14(4):369-81.
  • Nas S, Koyuncu M. Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach. Comput Math Methods Med. 2019;2019:4359719.
  • Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337-339.
  • Bajorath J, Kearnes S, Walters WP, Meanwell NA, Georg GI, Wang S. Artificial Intelligence in Drug Discovery: Into the Great Wide Open. J Med Chem. 2020;63(16):8651-52.
  • Keshavarzi Arshadi A, Webb J, Salem M, Cruz E, Calad-Thomson S, Ghadirian N et al. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Front Artif Intell. 2020;3:65.
  • Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One. 2020;15(2):e0229596.
  • Sappenfield JW, Smith WB, Cooper LA, Lizdas D, Gonsalves DB, Gravenstein N, Lampotang S, Robinson AR 3rd. Visualization Improves Supraclavicular Access to the Subclavian Vein in a Mixed Reality Simulator. Anesth Analg. 2018;127(1):83-9.
  • Mehta N, Devarakonda MV. Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey? J Allergy Clin Immunol. 2018;141(6):2019-2021.e1.
  • Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel). 2021;11(9):1523.
  • Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M et al. AI applications to medical images: From machine learning to deep learning. Phys Med. 2021;83:9-24.
  • Iqbal JD, Vinay R. Are we ready for Artificial Intelligence in Medicine? Swiss Med Wkly. 2022;152:w30179.
  • Kumar A, Gadag S, Nayak UY. The Beginning of a New Era: Artificial Intelligence in Healthcare. Adv Pharm Bull. 2021;11(3):414-25.
  • Tomita K, Nagao R, Touge H, Ikeuchi T, Sano H, Yamasaki A et al. Deep learning facilitates the diagnosis of adult asthma. Allergol Int. 2019;68(4):456-61.
  • Abelson S, Collord G, Ng SWK, Weissbrod O, Mendelson Cohen N, Niemeyer E et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature. 2018;559(7714):400-4.
  • McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
  • Zhang HT, Zhang JS, Zhang HH, Nan YD, Zhao Y, Fu EQ et al. Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. Eur J Nucl Med Mol Imaging. 2020;47(11):2525-32.
  • He YS, Su JR, Li Z, Zuo XL, Li YQ. Application of artificial intelligence in gastrointestinal endoscopy. J Dig Dis. 2019;20(12):623-30.
  • Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B et al. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc. 2020;13:2631774520935220.
  • Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S et al. Multi-modality artificial intelligence in digital pathology. Brief Bioinform. 2022;23(6):bbac367.
  • Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475(2):131-8.
  • Tae K. Transoral robotic thyroidectomy using the da Vinci single-port surgical system. Gland Surg. 2020;9(3):614-6.
  • Froiio C, Berlth F, Capovilla G, Tagkalos E, Hadzijusufovic E, Mann C et al. Robotic-assisted surgery for esophageal submucosal tumors: a single-center case series. Updates Surg. 2022;74(3):1043-54.
  • Tejo-Otero A, Buj-Corral I, Fenollosa-Artés F. 3D Printing in Medicine for Preoperative Surgical Planning: A Review. Ann Biomed Eng. 2020;48(2):536-55.
  • Feng ZH, Li XB, Phan K, Hu ZC, Zhang K, Zhao J et al. Design of a 3D navigation template to guide the screw trajectory in spine: a step-by-step approach using Mimics and 3-Matic software. J Spine Surg. 2018;4(3):645-53.
  • Corona PS, Vicente M, Tetsworth K, Glatt V. Preliminary results using patient-specific 3d printed models to improve preoperative planning for correction of post-traumatic tibial deformities with circular frames. Injury. 2018;49 Suppl 2:51-9.
  • Park JW, Kang HG, Kim JH, Kim HS. The application of 3D-printing technology in pelvic bone tumor surgery. J Orthop Sci. 2021;26(2):276-83.
  • Salah M, Tayebi L, Moharamzadeh K, Naini FB. Three-dimensional bio-printing and bone tissue engineering: technical innovations and potential applications in maxillofacial reconstructive surgery. Maxillofac Plast Reconstr Surg. 2020;42(1):18.
  • Shen M, Wang L, Gao Y, Feng L, Xu C, Li S et al. 3D bioprinting of in situ vascularized tissue engineered bone for repairing large segmental bone defects. Mater Today Bio. 2022;16:100382.
  • Creighton FX, Unberath M, Song T, Zhao Z, Armand M, Carey J. Early Feasibility Studies of Augmented Reality Navigation for Lateral Skull Base Surgery. Otol Neurotol. 2020 Aug;41(7):883-8.
  • Hu HZ, Feng XB, Shao ZW, Xie M, Xu S, Wu XH et al. Application and Prospect of Mixed Reality Technology in Medical Field. Curr Med Sci. 2019;39(1):1-6.
  • Wu X, Liu R, Yu J, Xu S, Yang C, Yang S et al. Mixed Reality Technology Launches in Orthopedic Surgery for Comprehensive Preoperative Management of Complicated Cervical Fractures. Surg Innov. 2018 Aug;25(4):421-22.
  • Yoshida S, Sugimoto M, Fukuda S, Taniguchi N, Saito K, Fujii Y. Mixed reality computed tomography-based surgical planning for partial nephrectomy using a head-mounted holographic computer. Int J Urol. 2019 Jun;26(6):681-2.
  • Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020 Feb;132(2):379-94.
  • Poncette AS, Mosch L, Spies C, Schmieding M, Schiefenhövel F, Krampe H et al. Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study. J Med Internet Res. 2020;22(6):e19091.
  • Angehrn Z, Haldna L, Zandvliet AS, Gil Berglund E, Zeeuw J, Amzal B et al. Artificial Intelligence and Machine Learning Applied at the Point of Care. Front Pharmacol. 2020;11:759.
  • Zhao Y, Liang C, Gu Z, Zheng Y, Wu Q. A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot. Int J Environ Res Public Health. 2020;17(8):2948.
  • Cheng N, Kuo A. Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time. Stud Health Technol Inform. 2020;272:199-202.
  • Lin YW, Zhou Y, Faghri F, Shaw MJ, Campbell RH. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PLoS One. 2019 Jul 8;14(7):e0218942.
  • Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80-93.
  • Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother. 2020;128:110255.
  • Awad A, Fina F, Goyanes A, Gaisford S, Basit AW. 3D printing: Principles and pharmaceutical applications of selective laser sintering. Int J Pharm. 2020;586:119594.
  • Mohanty S, Harun Ai Rashid M, Mridul M, Mohanty C, Swayamsiddha S. Application of Artificial Intelligence in COVID-19 drug repurposing. Diabetes Metab Syndr. 2020 Sep-Oct;14(5):1027-1031.
  • Russo G, Reche P, Pennisi M, Pappalardo F. The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opin Drug Discov. 2020;15(11):1267-81.
  • Dekker I, De Jong EM, Schippers MC, De Bruijn-Smolders M, Alexiou A, Giesbers B. Optimizing Students' Mental Health and Academic Performance: AI-Enhanced Life Crafting. Front Psychol. 2020;11:1063.
  • Wu D, Xiang Y, Wu X, Yu T, Huang X, Zou Y et al. Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship. Ann Transl Med. 2020;8(11):700.
  • Yang YY, Shulruf B. Expert-led and artificial intelligence (AI) system-assisted tutoring course increase confidence of Chinese medical interns on suturing and ligature skills: prospective pilot study. J Educ Eval Health Prof. 2019;16:7.
  • Bertin H, Huon JF, Praud M, Fauvel F, Salagnac JM, Perrin JP et al. Bilateral sagittal split osteotomy training on mandibular 3-dimensional printed models for maxillofacial surgical residents. Br J Oral Maxillofac Surg. 2020;58(8):953-8.
  • Bohl MA, McBryan S, Pais D, Chang SW, Turner JD, Nakaji P et al. The Living Spine Model: A Biomimetic Surgical Training and Education Tool. Oper Neurosurg (Hagerstown). 2020;19(1):98-106.
  • Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare. 2020:295–336.

Artificial Intelligence Applications in Medicine

Year 2022, , 604 - 613, 31.12.2022
https://doi.org/10.24938/kutfd.1214512

Abstract

Artificial Intelligence (AI) is a set of algorithms that exhibit human skills such as reasoning, learning, classification and creativity with the help of a machine. These AI algorithms attempt to simulate human intelligence through deep learning and machine learning. The increase and accessibility of data in the health sector has made recent successful AI applications possible. AI technology can bring great benefits to doctors' judgment and decision-making by bringing to the surface clinical information hidden under complex and big data. Although its wide clinical use is still limited, researches show that AI can be used successfully in the diagnosis, treatment, monitoring, classification of diseases, and differentiation of risky conditions. While it is not thought that AI can replace doctors in the future, it is envisioned to replace human judgment. In this review, the general outlines of artificial intelligence technology, its applications in health care, its future and possible ethical problems are overviewed.

Project Number

yok

References

  • Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021;41(6):1105-15.
  • Crossnohere NL, Elsaid M, Paskett J, Bose-Brill S, Bridges JFP. Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks. J Med Internet Res. 2022;24(8):e36823.
  • Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019;111(9):916-22.
  • Gong J, Liu JY, Sun XW, Zheng B, Nie SD. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules. Phys Med Biol. 2018;63(3):35036.
  • Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-75.
  • Sakagianni A, Feretzakis G, Kalles D, Koufopoulou C, Kaldis V. Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans. Stud Health Technol Inform. 2020;272:13-6.
  • Stoel BC. Artificial intelligence in detecting early RA. Semin Arthritis Rheum. 2019;49(3S):S25-S28.
  • Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. J Clin Med. 2019;8(11):1976.
  • Hwang Y, Lee HH, Park C, Tama BA, Kim JS, Cheung DY et al. Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network. Dig Endosc. 2021;33(4):598-607.
  • Hart SN, Flotte W, Norgan AP, Shah KK, Buchan ZR, Mounajjed T et al. Classification of Melanocytic Lesions in Selected and Whole-Slide Images via Convolutional Neural Networks. J Pathol Inform. 2019;10:5.
  • Kosaraju SC, Hao J, Koh HM, Kang M. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods. 2020;179:3-13.
  • Navarrete AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020;14(4):369-81.
  • Nas S, Koyuncu M. Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach. Comput Math Methods Med. 2019;2019:4359719.
  • Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337-339.
  • Bajorath J, Kearnes S, Walters WP, Meanwell NA, Georg GI, Wang S. Artificial Intelligence in Drug Discovery: Into the Great Wide Open. J Med Chem. 2020;63(16):8651-52.
  • Keshavarzi Arshadi A, Webb J, Salem M, Cruz E, Calad-Thomson S, Ghadirian N et al. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Front Artif Intell. 2020;3:65.
  • Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One. 2020;15(2):e0229596.
  • Sappenfield JW, Smith WB, Cooper LA, Lizdas D, Gonsalves DB, Gravenstein N, Lampotang S, Robinson AR 3rd. Visualization Improves Supraclavicular Access to the Subclavian Vein in a Mixed Reality Simulator. Anesth Analg. 2018;127(1):83-9.
  • Mehta N, Devarakonda MV. Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey? J Allergy Clin Immunol. 2018;141(6):2019-2021.e1.
  • Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel). 2021;11(9):1523.
  • Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M et al. AI applications to medical images: From machine learning to deep learning. Phys Med. 2021;83:9-24.
  • Iqbal JD, Vinay R. Are we ready for Artificial Intelligence in Medicine? Swiss Med Wkly. 2022;152:w30179.
  • Kumar A, Gadag S, Nayak UY. The Beginning of a New Era: Artificial Intelligence in Healthcare. Adv Pharm Bull. 2021;11(3):414-25.
  • Tomita K, Nagao R, Touge H, Ikeuchi T, Sano H, Yamasaki A et al. Deep learning facilitates the diagnosis of adult asthma. Allergol Int. 2019;68(4):456-61.
  • Abelson S, Collord G, Ng SWK, Weissbrod O, Mendelson Cohen N, Niemeyer E et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature. 2018;559(7714):400-4.
  • McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
  • Zhang HT, Zhang JS, Zhang HH, Nan YD, Zhao Y, Fu EQ et al. Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. Eur J Nucl Med Mol Imaging. 2020;47(11):2525-32.
  • He YS, Su JR, Li Z, Zuo XL, Li YQ. Application of artificial intelligence in gastrointestinal endoscopy. J Dig Dis. 2019;20(12):623-30.
  • Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B et al. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc. 2020;13:2631774520935220.
  • Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S et al. Multi-modality artificial intelligence in digital pathology. Brief Bioinform. 2022;23(6):bbac367.
  • Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475(2):131-8.
  • Tae K. Transoral robotic thyroidectomy using the da Vinci single-port surgical system. Gland Surg. 2020;9(3):614-6.
  • Froiio C, Berlth F, Capovilla G, Tagkalos E, Hadzijusufovic E, Mann C et al. Robotic-assisted surgery for esophageal submucosal tumors: a single-center case series. Updates Surg. 2022;74(3):1043-54.
  • Tejo-Otero A, Buj-Corral I, Fenollosa-Artés F. 3D Printing in Medicine for Preoperative Surgical Planning: A Review. Ann Biomed Eng. 2020;48(2):536-55.
  • Feng ZH, Li XB, Phan K, Hu ZC, Zhang K, Zhao J et al. Design of a 3D navigation template to guide the screw trajectory in spine: a step-by-step approach using Mimics and 3-Matic software. J Spine Surg. 2018;4(3):645-53.
  • Corona PS, Vicente M, Tetsworth K, Glatt V. Preliminary results using patient-specific 3d printed models to improve preoperative planning for correction of post-traumatic tibial deformities with circular frames. Injury. 2018;49 Suppl 2:51-9.
  • Park JW, Kang HG, Kim JH, Kim HS. The application of 3D-printing technology in pelvic bone tumor surgery. J Orthop Sci. 2021;26(2):276-83.
  • Salah M, Tayebi L, Moharamzadeh K, Naini FB. Three-dimensional bio-printing and bone tissue engineering: technical innovations and potential applications in maxillofacial reconstructive surgery. Maxillofac Plast Reconstr Surg. 2020;42(1):18.
  • Shen M, Wang L, Gao Y, Feng L, Xu C, Li S et al. 3D bioprinting of in situ vascularized tissue engineered bone for repairing large segmental bone defects. Mater Today Bio. 2022;16:100382.
  • Creighton FX, Unberath M, Song T, Zhao Z, Armand M, Carey J. Early Feasibility Studies of Augmented Reality Navigation for Lateral Skull Base Surgery. Otol Neurotol. 2020 Aug;41(7):883-8.
  • Hu HZ, Feng XB, Shao ZW, Xie M, Xu S, Wu XH et al. Application and Prospect of Mixed Reality Technology in Medical Field. Curr Med Sci. 2019;39(1):1-6.
  • Wu X, Liu R, Yu J, Xu S, Yang C, Yang S et al. Mixed Reality Technology Launches in Orthopedic Surgery for Comprehensive Preoperative Management of Complicated Cervical Fractures. Surg Innov. 2018 Aug;25(4):421-22.
  • Yoshida S, Sugimoto M, Fukuda S, Taniguchi N, Saito K, Fujii Y. Mixed reality computed tomography-based surgical planning for partial nephrectomy using a head-mounted holographic computer. Int J Urol. 2019 Jun;26(6):681-2.
  • Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020 Feb;132(2):379-94.
  • Poncette AS, Mosch L, Spies C, Schmieding M, Schiefenhövel F, Krampe H et al. Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study. J Med Internet Res. 2020;22(6):e19091.
  • Angehrn Z, Haldna L, Zandvliet AS, Gil Berglund E, Zeeuw J, Amzal B et al. Artificial Intelligence and Machine Learning Applied at the Point of Care. Front Pharmacol. 2020;11:759.
  • Zhao Y, Liang C, Gu Z, Zheng Y, Wu Q. A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot. Int J Environ Res Public Health. 2020;17(8):2948.
  • Cheng N, Kuo A. Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time. Stud Health Technol Inform. 2020;272:199-202.
  • Lin YW, Zhou Y, Faghri F, Shaw MJ, Campbell RH. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PLoS One. 2019 Jul 8;14(7):e0218942.
  • Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80-93.
  • Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother. 2020;128:110255.
  • Awad A, Fina F, Goyanes A, Gaisford S, Basit AW. 3D printing: Principles and pharmaceutical applications of selective laser sintering. Int J Pharm. 2020;586:119594.
  • Mohanty S, Harun Ai Rashid M, Mridul M, Mohanty C, Swayamsiddha S. Application of Artificial Intelligence in COVID-19 drug repurposing. Diabetes Metab Syndr. 2020 Sep-Oct;14(5):1027-1031.
  • Russo G, Reche P, Pennisi M, Pappalardo F. The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opin Drug Discov. 2020;15(11):1267-81.
  • Dekker I, De Jong EM, Schippers MC, De Bruijn-Smolders M, Alexiou A, Giesbers B. Optimizing Students' Mental Health and Academic Performance: AI-Enhanced Life Crafting. Front Psychol. 2020;11:1063.
  • Wu D, Xiang Y, Wu X, Yu T, Huang X, Zou Y et al. Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship. Ann Transl Med. 2020;8(11):700.
  • Yang YY, Shulruf B. Expert-led and artificial intelligence (AI) system-assisted tutoring course increase confidence of Chinese medical interns on suturing and ligature skills: prospective pilot study. J Educ Eval Health Prof. 2019;16:7.
  • Bertin H, Huon JF, Praud M, Fauvel F, Salagnac JM, Perrin JP et al. Bilateral sagittal split osteotomy training on mandibular 3-dimensional printed models for maxillofacial surgical residents. Br J Oral Maxillofac Surg. 2020;58(8):953-8.
  • Bohl MA, McBryan S, Pais D, Chang SW, Turner JD, Nakaji P et al. The Living Spine Model: A Biomimetic Surgical Training and Education Tool. Oper Neurosurg (Hagerstown). 2020;19(1):98-106.
  • Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare. 2020:295–336.
There are 60 citations in total.

Details

Primary Language Turkish
Subjects Health Care Administration
Journal Section Review
Authors

Hatice Keleş 0000-0003-1460-2099

Project Number yok
Publication Date December 31, 2022
Submission Date December 4, 2022
Published in Issue Year 2022

Cite

APA Keleş, H. (2022). TIPTA YAPAY ZEKA UYGULAMALARI. The Journal of Kırıkkale University Faculty of Medicine, 24(3), 604-613. https://doi.org/10.24938/kutfd.1214512
AMA Keleş H. TIPTA YAPAY ZEKA UYGULAMALARI. Kırıkkale Üni Tıp Derg. December 2022;24(3):604-613. doi:10.24938/kutfd.1214512
Chicago Keleş, Hatice. “TIPTA YAPAY ZEKA UYGULAMALARI”. The Journal of Kırıkkale University Faculty of Medicine 24, no. 3 (December 2022): 604-13. https://doi.org/10.24938/kutfd.1214512.
EndNote Keleş H (December 1, 2022) TIPTA YAPAY ZEKA UYGULAMALARI. The Journal of Kırıkkale University Faculty of Medicine 24 3 604–613.
IEEE H. Keleş, “TIPTA YAPAY ZEKA UYGULAMALARI”, Kırıkkale Üni Tıp Derg, vol. 24, no. 3, pp. 604–613, 2022, doi: 10.24938/kutfd.1214512.
ISNAD Keleş, Hatice. “TIPTA YAPAY ZEKA UYGULAMALARI”. The Journal of Kırıkkale University Faculty of Medicine 24/3 (December 2022), 604-613. https://doi.org/10.24938/kutfd.1214512.
JAMA Keleş H. TIPTA YAPAY ZEKA UYGULAMALARI. Kırıkkale Üni Tıp Derg. 2022;24:604–613.
MLA Keleş, Hatice. “TIPTA YAPAY ZEKA UYGULAMALARI”. The Journal of Kırıkkale University Faculty of Medicine, vol. 24, no. 3, 2022, pp. 604-13, doi:10.24938/kutfd.1214512.
Vancouver Keleş H. TIPTA YAPAY ZEKA UYGULAMALARI. Kırıkkale Üni Tıp Derg. 2022;24(3):604-13.

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