The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.
Artificial neural networks in medical diagnosis
Open accessAbstract
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples.
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“The ability of the NN is to model highly nonlinear systems such as physiological records where the correlation of the input parameters is not easily detectable [71].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [71]
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Artificial neural networks: fundamentals, computing, design, and application
Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,316
Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y.-C., Baker, J. A., & Tourassi, G. D. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.12.031 · Cited by 797
Neural networks in chemistry and drug design
Zupan, J., & Gasteiger, J. (1999) · Medical Entomology and Zoology · Cited by 718
An introduction to neural computing
(1990) · Biological Psychology · doi:10.1016/0301-0511(90)90019-s · Cited by 621
Kubát, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-63913-0_5 · Cited by 379
Artificial Intelligence in Medical Diagnosis
Szolovits, P., Patil, R. S., & Schwartz, W. B. (1988) · Annals of Internal Medicine · doi:10.7326/0003-4819-108-1-80 · Cited by 285
Feature selection with neural networks
Verikas, A., & Bačauskienė, M. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00081-8 · Cited by 253
Tate, A. R., Underwood, J., Acosta, D., Julià‐Sapé, M., Majós, C., Moreno‐Torres, À., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., et al. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1016 · Cited by 227
Artificial neural networks for diagnosis and survival prediction in colon cancer
Ahmed, F. (2005) · Molecular Cancer · doi:10.1186/1476-4598-4-29 · Cited by 223
Segmentation of multiple sclerosis lesions in MR images: a review
Mortazavi, D., Kouzani, A. Z., & Hamid Soltanian-zadeh, A. (2011) · Neuroradiology · doi:10.1007/s00234-011-0886-7 · Cited by 146
Research works printed but not linked: 5
- 10.1371/journal.pone.0044587.t004 · DOI
- 10.1002/1097-0142(20010415)91:8+%3C1615::AID-CNCR1175%3E3.0.CO;2-L · DOI
- Gannous AS, 2011, Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques, WASET, 50, 124
- 10.1371/journal.pone.0029179.t004 · DOI
- 10.1007/s00234-011-0886-7 · DOI
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Metadata
- Authors
- Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J.
- Affiliations
- Masaryk University (CZ)
- Universidad de Salamanca (ES)
- Venue
- Journal of Applied Biomedicine
- Published
- 2013-07-31
- Volume
- 11
- Issue
- 2
- Pages
- 47-58
- ISSN
- 1214-021X (ISSN-L)
- 1214-0287
- OpenAlex
- W2137687977
- Cited by
- 898
- References
- 58
- Citations by year
- 2026: 7 · 2025: 46 · 2024: 71 · 2023: 69 · 2022: 89 · 2021: 107 · 2020: 104 · 2019: 93 · 2018: 104 · 2017: 70 · 2016: 62 · 2015: 36 · 2014: 32 · 2013: 5
- FWCI
- 77.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Applied Biomedicine (publishedVersion)
- Funders
- Universidad de La Laguna
- Grantová Agentura České Republiky (MSM0021627501, CZ.1.05/1.1.00/02.0123, MSM0021622430, CZ.1.05/2.1.00/03.0086, GA202/07/1669, GA104/08/0229)
- European Regional Development Fund (MSM0021622430, CZ.1.05/1.100, CZ.1.07/2.3.00/20.0185, CZ.1.05/2.1.00/03.0086, CZ.1.05/1.1.00/02.0123.)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial neural networks, computer-aided diagnosis, diagnosis, clinical decision support, adaptive learning algorithms, misdiagnosis prevention
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