Artificial Intelligence Augmentation in Blood Transfusion, Biochemistry, and Hematology of Digital Pathology: A Comparative Performance Evaluation on Pathology Labs and Corporate Hospitals located in Bengaluru
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Senthilkumar, Gagan Kumar B R, Lasya K R
|DOI : 10.14445/22315381/IJETT-V68I12P222|
MLA Style: Senthilkumar, Gagan Kumar B R, Lasya K R. Artificial Intelligence Augmentation in Blood Transfusion, Biochemistry, and Hematology of Digital Pathology: A Comparative Performance Evaluation on Pathology Labs and Corporate Hospitals located in Bengaluru International Journal of Engineering Trends and Technology 68.12(2020):132-139.
APA Style:Senthilkumar, Gagan Kumar B R, Lasya K R. Artificial Intelligence Augmentation in Blood Transfusion, Biochemistry, and Hematology of Digital Pathology: A Comparative Performance Evaluation on Pathology Labs and Corporate Hospitals located in Bengaluru International Journal of Engineering Trends and Technology, 68(12), 132-139.
Artificial intelligence augmentation is increasingly incorporated in the medical and healthcare sphere, especially in the pathology arena of Blood transfusion, Biochemistry, and Hematology, towards detecting and analyzing diseases and disorders. Hence, it is important to understand the implications of artificial intelligence on humanity with the pathology`s innovation practices. The pathology laboratories and hospitals need to incorporate artificial intelligence into specific functions and sub-functions of result-oriented specialties to create effortless healthcare activities. This study was undertaken with selected pathology laboratories and healthcare organizations in Bengaluru that have access to digital initiatives and artificial intelligence into their business process for the last three years and examine the performance on three result-oriented functions in which artificial intelligence is incorporated. This article studies on the potential competencies of artificial intelligence augmentation and builds awareness in the healthcare sector on the capabilities of augmenting the right intelligent systems enables in streamlining the activities carried out in the pathology lab and exhibits that on what organization need and how technological advancement and innovation helps in improving the organizational performance and notifies that if the performance improvement measures are followed with continuous update and maintenance, only then innovations in healthcare result in success.
 Ashok A, Judi Saud. A Review on machine learning approach to Detection of diagnostic results in hospitals. Global Journal of Medical Innovations, BIES. (2014) 32-41.
 Alausxion, Dainal Wayne, Fredric GR. Prediction using logistic regression for measuring the pathological results on the health technologies, Report No. 119549, Fargo: California State University; (2012), IEEE J. Quantum Electron.,
 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ, eds. Advances in neural information processing systems 25. Red Hook: Curran Associates, (2012) 1097–105.
 Mnih, KK, Silver D Silva, et al. Human-level control through deep reinforcement learning. Nature. 5(18) (2015) 529–533.
 Silver D, Schrittwieser J, Simonyan K, et al. Mastering Go`s game without human knowledge. Nature. 550 (2017) 354–359.
 Hannun A, Case C, Casper J. Deep speech: scaling up end-to-end speech recognition [Internet] Ithaca: arXiv, Cornell University; 2014.
 Luong MT, Pham H, Manning CD. Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing; 2015 Sep 17-21; Lisbon, Portugal. Stroudsburg. (2015) 1412–1421.
 Wu Y, Schuster M, Chen Z. Google’s neural machine translation system: bridging the gap between human and machine translation Cornell University; 2016.
 Antol S, Agrawal A, Lu J, et al. VQA: visual question answering. In: Proceedings of the IEEE International Conference on Computer Vision; 2015 Dec 7-13; Santiago, Chile. Washington, DC. (2015) 2425–2433.
 Kim JH, Lee SW, Kwak D, et al. Multimodal residual learning for visual QA. In: Lee DD, von Luxburg U, Garnett R, et al., editors. Advances in neural information processing systems 29. Red Hook: NY Curran Associates Inc; (2016) 361–9.
 LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 86 (1998):2278–2324.
 Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015 Jun 7-12, Boston, MA, USA. Silver Spring: IEEE Computer Society Press; (2015). 1–9.
 Weizenbaum J. ELIZA: a computer program for the study of natural language communication between man and machine. Communication ACM. 9 (1966) 36–45.
 P. Seetha Subha Priya, S. Nandhinidevi, Dr. M. Thangamani, Dr. S. Nallusamy A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis International Journal of Engineering Trends and Technology 68(10)(2020) 63-66.
 Shortliffe EH. Mycin: a knowledge-based computer program applied to infectious diseases. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, 1977 Oct 3-5, Washington, DC, USA. New York: Institute of Electrical and Electronics Engineers; (1977) 66–9.
 Heckerman DE, Nathwani BN. Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inference. Methods Inf Med. 31 (1992)106–16.
 Sharma G, Carter A. Artificial intelligence and the pathologist: Future frenemies? Arch Pathol Lab Med 141 (2017)622-633.
 Holzinger A, Malle B, Kieseberg P, Roth PM, Müller H, Reihs R, et al. Towards the augmented pathologist: Challenges of explainable-ai in digital pathology. arXiv Preprint arXiv: 1712.06657; 2017.
 Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 19 (2017) 221-248.
 Wong ST. Is pathology prepared for the adoption of artificial intelligence? Cancer Cytopathol 2018;126:373-5.
 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8.
 Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017;318:2199-210.
 Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A 2018;115:E2970-E2979.
 Robertson S, Azizpour H, Smith K, Hartman J. Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Transl Res 2018;194:19-35.
 Xu Y, Jia Z, Wang LB, Ai Y, Zhang F, Lai M, et al. Large-scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics 2017;18:281.
Artificial Intelligence, Augmentation, Blood Transfusion, Biochemistry, Hematology, Pathology