The DOI matches its Crossref record; 197 of the 197 references it cites were found in a registry or an index.
A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis
Open accessAbstract
Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.
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“Research by Kumar et al. (76) and Zhang et al. (77) examined the use of AI algorithms in cancer and diagnosis. The studies revealed that AI algorithms achieved high accuracy rates in diagnosing cancer subtypes, assisting clinicians in developing tailored treatment plans.”
From Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. (2023) · cites this paper as [76]
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Metadata
- Authors
- Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C.
- Affiliations
- Indus University (IN)
- Providence University (TW)
- Corresponding author
- Kumar, Yogesh
- Gupta, Surbhi
- Singla, Ruchi
- Hu, Yu-Chen
- Venue
- Archives of Computational Methods in Engineering
- Published
- 2021-09-27
- Volume
- 29
- Issue
- 4
- Pages
- 2043-2070
- ISSN
- 1134-3060 (ISSN-L)
- 1886-1784
- PMC
- PMC8475374
- OpenAlex
- W3202138229
- Cited by
- 223
- References
- 194
- Citations by year
- 2026: 15 · 2025: 53 · 2024: 65 · 2023: 57 · 2022: 29 · 2021: 4
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Archives of Computational Methods in Engineering (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence, cancer prediction, cancer diagnosis, deep learning, machine learning classification, cancer mortality, diagnostic accuracy, medical image analysis
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