Exploring AI in Healthcare: How the Acceleration of Data Processing can Impact Life Saving Diagnoses

Artificial Intelligence (AI) is one of the biggest topics being discussed in the realm of Computer Science and it has made incredible breakthroughs possible in so many different industries. One of the largest issues with utilizing computational resources in the health industry historically is centered around the quantity of data, the specificity of conditions for accurate results, and the general risks associated with being incorrect in an analysis. Although these all have been major issues in the past, the application of artificial intelligence has opened up an entirely different realm of possibilities because accessing massive amounts of patient data, is essential for generating an extremely accurate model in machine learning (ML). This paper presents an analysis of tools and algorithm design techniques used in recent times to accelerate data processing in the realm of healthcare, but one of the most important discoveries is that the standardization of conditioned data being fed into the models is almost more important than the algorithms or tools being used combined.

Content

Author and article information

Contributors

Jacob Janes

Ajay Bansal

Tyler Baron

Conference

Publication date (Print): July 2022

Pages: 1-6

Affiliations

[0001]School of Computing and Augmented Intelligence

Arizona State University

Mesa, Arizona

Article

DOI: 10.14236/ewic/ODAK22.2

SO-VID: b2af8845-bff5-436c-8fd1-667bbac23a2b

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK

Conference acronym: ODAK 2022

Conference number:

Conference location: Brighton, UK

Conference date: June 30-July 1, 2022

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Open Data and Knowledge for a Post-Pandemic Era

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/ODAK22.2

Self URI (journal page): https://ewic.bcs.org/

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Comments

Comment on this article

[1] Tiranee Achalakul "Using Big Data to Improve Healthcare Services." 2018 https://www.youtube.com/watch?v=7t75CNC34vU

[2] Casey Bennett "AI in Healthcare — It's about Time." TEDx Nashville 2016 https://www.youtube.com/watch?v=3LkbUxqGTfo

[3] Casey C BennettKris Hauser “Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach.” Artificial Intelligence in Medicine 57 1 Elsevier B.V 2012 9 19.

[4] Gillian Ellison “EGFR mutation testing in lung cancer: a review of available methods & their use for analysis of tumor tissue.” Journal of clinical pathology 66,2 2013 79 89

[5] Laure Gossec “Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology.” Arthritis Care & Research 2010 71 10 WILEY, 2019 1336 43

[6] T HügleM Kalweit Artificial intelligence-assisted treatment in rheumatology Z Rheumatol 80 914 927 2021 https://doiorg.ezproxy1.lib.asu.edu/10.1007/s00393-021-01096-y

[7] Petros Kalendralis “External Validation of a Bayesian Network for Error Detection in Radiotherapy Plans.” IEEE Transactions on Radiation and Plasma Medical Sciences 6 2 IEEE 2022 200 06

[8] Suruchi Kothari “Artificial Intelligence (AI) and Rheumatology: a Potential Partnership.” Rheumatology (Oxford, England) 58 11 Oxford Univ Press 2019 1894 95

[9] Mykola Kozlenko "Machine learning services in the cloud." http://lib.pnu.edu.ua:8080/bitstream/123456789/10786/1/pnu_annual_2020_kozlenko.pdf 2021

[10] Gloria Hyunjung KwakPan Hui DeepHealth: Review and Challenges of Artificial Intelligence in Health Informatics 2019

[11] Zhanyu Ma “IEEE Access Special Section Editorial: AIDriven Big Data Processing: Theory, Methodology, and Applications.” IEEE Access 8 IEEE 2020 199882 98

[12] David Moner "Archetype-based semantic integration and standardization of clinical data." 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE 2006

[13] David Pierce Mysona “Applying Artificial Intelligence to Gynecologic Oncology: A Review.” Obstetrical & Gynecological Survey 76 5 2021 292 301

[14] Majid Nour “A Novel Medical Diagnosis Model for COVID-19 Infection Detection Based on Deep Features & Bayesian Optimization.” Applied Soft Computing 97 2020 106580

[15] Dana E Orange “Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data.” Arthritis & Rheumatology 70 5 WILEY 2018 690 701

[16] Arjun Panesar Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes Second APress 2021

[17] Tania Pereira “Sharing Biomedical Data: Strengthening AI Development in 'Healthcare.” Healthcare (Basel) 9 7 MDPI 2021 827

[18] A RamezanpourA.L BeamJ.H ChenA Mashaghi Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms Diagnostics 2020 10 972

[19] Y SimMJ ChungE Kotter 2019 Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs Radiology 294 199 209

[20] Jasjit S Suri "Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective," IEEE Journal of Biomedical and Health Informatics 25 11 4128 4139 Nov 2021

[21] D Tihanyi “AI Oncology Algorithm-Based Prioritization of EGFR Inhibitors in Case of Rare EGFR Mutations.” Annals of Oncology 30 2019 vii30

[22] Ferhat UcarDeniz Korkmaz “COVIDiagnosis-Net: Deep Bayes-SqueezeNet Based Diagnosis of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images.” Medical Hypotheses 140 Elsevier 2020 109761 109761

[23] Hyunsuk Yoo “AI-Based Improvement in Lung Cancer Detection on Chest Radiographs: Results of a Multi-Reader Study in NLST Dataset.” European Radiology 31 12 Springer Berlin Heidelberg 2021 9664 74

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