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Cited by 255

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Open access
Kaur, Simarjeet, Singla, Jimmy, Nkenyereye, Lewis, Jha, Sudan, Prashar, Deepak, Joshi, Gyanendra Prasad, El-Sappagh, Shaker, Islam, Md. Saiful, Islam, S. M. Riazul·2020·IEEE Access
DOI 10.1109/access.2020.3042273
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Abstract

Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

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Research works printed but not linked: 67

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Metadata

Authors
Kaur, S., Singla, J., Nkenyereye, L., Jha, S., Prashar, D., Joshi, G. P., El-Sappagh, S., Islam, M. S., & Islam, S. M. R.
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
228049-228069
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2020.3042273
OpenAlex
W3108597174
Cited by
255
References
12
FWCI
14.35
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/legalcode (vor)
Funders
  • National Research Foundation of Korea grant
  • Korean Government, Ministry of Science and ICT (NRF-2020R1A2B5B02002478)
  • Sejong University through its Faculty Research Program

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CoChat. (2026). Citation check: Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives. https://app.cochat.ai/papers/10.1109/access.2020.3042273
The paper
Kaur, S., Singla, J., Nkenyereye, L., Jha, S., Prashar, D., Joshi, G. P., El-Sappagh, S., Islam, M. S., & Islam, S. M. R. (2020). Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives. IEEE Access. https://doi.org/10.1109/access.2020.3042273

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.