Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts’ knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients’ data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.
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McLeod, R., and Schell, G. P., Management information systems. Pearson/Prentice Hall, USA, 2007.
Turban, J., Rainer, R., and Potter, R., Introduction to information technology. Wiley, New York, 2005.
Goletsis, Y., Papaloukas, C., Fotiadis, D., Likas, A., and Michalis, L., Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis. IEEE Trans. Biomed. Eng. 51:1717–1725, 2004.
Rainer, S., and Lothar, G., Case-based reasoning for antibiotics therapy advice: an investigation in retrieval algorithms and prototypes. Artif. Intell. Med. 23:171–176, 2001.
Chi, C. L., Nick Street, W., and Ward, M., Building a hospital referral expert system with a prediction and optimization-based decision support system algorithm. J. Biomed. Inform. 41:371–386, 2008.
Sharaf-El-Deen, D. A., Moawad, I. F., and Khalifa, M. E., A new hybrid case-based reasoning approach for medical diagnosis systems. J. Med. Syst. 38:9, 2014.
Kunhimangalam, R., Ovallath, S., and Joseph, P. K., A clinical decision support system with an integrated EMR for diagnosis of peripheral neuropathy. J. Med. Syst. 38:38, 2014.
Wells, D. M., and Niedere, J., A medical expert system approach using artificial neural networks for standardized treatment planning. Int. J. Radiat. Oncol. Biol. Phys. 41:173–182, 1998.
Hanson, W., and Marshall, B., Artificial intelligence applications in the intensive care unit. Crit. Care Med. 29:427–437, 2001.
Bascil, M. S., and Temurtas, F., A study on hepatitis disease diagnosis using multilayer neural network with Levenberg Marquardt training algorithm. J. Med. Syst. 35:433–436, 2011.
Ertl, L., and Christ, F., Significant improvement of the quality of bystander first aid using an expert system with a mobile multimedia device. Resuscitation 74:286–295, 2007.
Sari, M., Gulbandilar, E., and Cimbiz, A., Prediction of low back pain with two expert systems. J. Med. Syst. 36:1523–1527, 2012.
Maizels, M., and Wolfe, W., An expert system for headache diagnosis: The computerized headache assessment tool (CHAT). Headache 48:72–78, 2008.
Elveren, E., and Yumusak, N., Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J. Med. Syst. 35:329–332, 2011.
Fisher, A. C., Chandna, A., and Cunningham, I. P., The differential diagnosis of vertical strabismus from prism cover test data using an artificially intelligent expert system. Med. Biol. Eng. Comput. 45:689–693, 2007.
Bascil, M. S., and Oztekin, H., A study on hepatitis disease diagnosis using probabilistic neural network. J. Med. Syst. 36:1603–1606, 2012.
Basciftci, F., and Incekara, H., Design of web-based fuzzy input expert system for the analysis of serology laboratory tests. J. Med. Syst. 36:2187–2191, 2012.
Lam, C. F. D., Leung, K. S., Heng, P. A., Lim, C. E. D., and Wong, F. W. S., Chinese acupuncture expert system (CAES): A useful tool to practice and learn medical acupuncture. J. Med. Syst. 36:1883–1890, 2012.
Issac Niwas, S., Palanisamy, P., Chibbar, R., and Zhang, W. J., An expert support system for breast cancer diagnosis using color wavelet features. J. Med. Syst. 36:3091–3102, 2012.
Benali, R., Reguig, F. B., and Slimane, Z. H., Automatic classification of heartbeats using wavelet neural network. J. Med. Syst. 36:883–892, 2012.
Exarchos, T. P., Tsipouras, M. G., Exarchos, C. P., Papaloukas, C., Fotiadis, D., and Michalis, L. K., A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif. Intell. Med. 40:187–200, 2007.
Kumar, S. J. J., and Madheswaran, M., An improved medical decision support system to identify the diabetic retinopathy using fundus images. J. Med. Syst. 36:3573–3581, 2012.
Huang, M. L., Hung, Y. H., Lee, W. M., Li, R. K., and Wang, T. H., Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. J. Med. Syst. 36:407–414, 2012.
Feng, F., Wu, Y., Wu, Y., Nie, G., and Ni, R., The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer. J. Med. Syst. 36:2973–2980, 2012.
Avci, E., A new expert system for diagnosis of lung cancer: GDAGÇöLS_SVM. J. Med. Syst. 36:2005–2009, 2012.
Niruii, M., Abdolmaleki, P., and Giti, M., A hybrid simulation model of ANN and genetic algorithms for detection of benign and malignant breast masses. Iran. J. Med. Phys. 13:67–80, 2007.
Abbod, M. F., Catto, J., Linkens, D., and Hamdy, F., Application of artificial intelligence to the management of urological cancer. J. Urol. 178:1150–1156, 2007.
Kumar, H., A fuzzy expert system design for analysis of body sounds and design of an unique electronic stethoscope (development of HILSA kit). Biosens. Bioelectron. 22:1121–1125, 2007.
Keles, A., and Keles, A., ESTDD: Expert system for thyroid diseases diagnosis. Expert Syst. Appl. 34:242–246, 2008.
Chen, H. L., Yang, B., Wang, G., Liu, J., Chen, Y. D., and Liu, D. Y., A three-stage expert system based on support vector machines for thyroid disease diagnosis. J. Med. Syst. 36:1953–1963, 2012.
Luciani, D., Cavuto, S., and Antiga, L., Bayes pulmonary embolism assisted diagnosis: A new expert system for clinical use. Emerg. Med. J. 24:157–164, 2007.
Polat, K., and Gunes, S., Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine. Appl. Math. Comput. 186:898–906, 2007.
Amodio, P., Pellegrini, A., Ubiali, E., Mathy, I., Del Piccolo, F., Orsato, R., et al., The EEG assessment of low-grade hepatic encephalopathy: Comparison of an artificial neural network-expert system (ANNES) based evaluation with visual EEG readings and EEG spectral analysis. Clin. Neurophysiol. 117:2243–2255, 2006.
Raoufy, M., Vahdani, P., Alavian, S., Fekri, S., Eftekhari, P., and Gharibzadeh, S., A novel method for diagnosing cirrhosis in patients with chronic hepatitis b: Artificial neural network approach. J. Med. Syst. 35:121–126, 2011.
Sengur, A., An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Syst. Appl. 35:214–222, 2008.
Shortliffe, E. H., and Cimino, J. J., Biomedical informatics. Springer, New York, 2006.
Gröndahl, H., Are agency and responsibility still solely ascribable to humans? The case of medical decision support systems. In: Duquenoy, P., George, C., and Kimppa, K. (Eds.), Ethical, legal, and social issues in medical informatics. Medical Information Science Reference, Hershey, PA, pp. 85–112, 2008.
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The authors declare that they have no conflict of interest.
This article is part of the Topical Collection on Transactional Processing Systems
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Sheikhtaheri, A., Sadoughi, F. & Hashemi Dehaghi, Z. Developing and Using Expert Systems and Neural Networks in Medicine: A Review on Benefits and Challenges. J Med Syst 38, 110 (2014). https://doi.org/10.1007/s10916-014-0110-5
- Artificial intelligence
- Clinical decision making
- Expert systems
- Knowledge-based systems
- Neural networks