Developing and Using Expert Systems and Neural Networks in Medicine: A Review on Benefits and Challenges


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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  1. 1.

    McLeod, R., and Schell, G. P., Management information systems. Pearson/Prentice Hall, USA, 2007.

    Google Scholar 

  2. 2.

    Turban, J., Rainer, R., and Potter, R., Introduction to information technology. Wiley, New York, 2005.

    Google Scholar 

  3. 3.

    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.

    Article  Google Scholar 

  4. 4.

    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.

    Article  Google Scholar 

  5. 5.

    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.

    Article  Google Scholar 

  6. 6.

    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.

    Article  Google Scholar 

  7. 7.

    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.

    Article  Google Scholar 

  8. 8.

    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.

    Article  Google Scholar 

  9. 9.

    Hanson, W., and Marshall, B., Artificial intelligence applications in the intensive care unit. Crit. Care Med. 29:427–437, 2001.

    Article  Google Scholar 

  10. 10.

    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.

    Article  Google Scholar 

  11. 11.

    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.

    Article  Google Scholar 

  12. 12.

    Sari, M., Gulbandilar, E., and Cimbiz, A., Prediction of low back pain with two expert systems. J. Med. Syst. 36:1523–1527, 2012.

    Article  Google Scholar 

  13. 13.

    Maizels, M., and Wolfe, W., An expert system for headache diagnosis: The computerized headache assessment tool (CHAT). Headache 48:72–78, 2008.

    Article  Google Scholar 

  14. 14.

    Elveren, E., and Yumusak, N., Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J. Med. Syst. 35:329–332, 2011.

    Article  Google Scholar 

  15. 15.

    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.

    Article  Google Scholar 

  16. 16.

    Bascil, M. S., and Oztekin, H., A study on hepatitis disease diagnosis using probabilistic neural network. J. Med. Syst. 36:1603–1606, 2012.

    Article  Google Scholar 

  17. 17.

    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.

    Article  Google Scholar 

  18. 18.

    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.

    Article  Google Scholar 

  19. 19.

    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.

    Article  Google Scholar 

  20. 20.

    Benali, R., Reguig, F. B., and Slimane, Z. H., Automatic classification of heartbeats using wavelet neural network. J. Med. Syst. 36:883–892, 2012.

    Article  Google Scholar 

  21. 21.

    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.

    Article  Google Scholar 

  22. 22.

    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.

    Article  Google Scholar 

  23. 23.

    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.

    Article  Google Scholar 

  24. 24.

    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.

    Article  Google Scholar 

  25. 25.

    Avci, E., A new expert system for diagnosis of lung cancer: GDAGÇöLS_SVM. J. Med. Syst. 36:2005–2009, 2012.

    Article  Google Scholar 

  26. 26.

    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.

    Google Scholar 

  27. 27.

    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.

    Article  Google Scholar 

  28. 28.

    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.

    Article  Google Scholar 

  29. 29.

    Keles, A., and Keles, A., ESTDD: Expert system for thyroid diseases diagnosis. Expert Syst. Appl. 34:242–246, 2008.

    Article  Google Scholar 

  30. 30.

    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.

    Article  Google Scholar 

  31. 31.

    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.

    Article  Google Scholar 

  32. 32.

    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.

    Article  MATH  MathSciNet  Google Scholar 

  33. 33.

    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.

    Article  Google Scholar 

  34. 34.

    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.

    Article  Google Scholar 

  35. 35.

    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.

    Article  Google Scholar 

  36. 36.

    Shortliffe, E. H., and Cimino, J. J., Biomedical informatics. Springer, New York, 2006.

    Google Scholar 

  37. 37.

    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.

    Google Scholar 

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The authors declare that they have no conflict of interest.

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Correspondence to Abbas Sheikhtaheri.

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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).

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  • Artificial intelligence
  • Clinical decision making
  • Expert systems
  • Knowledge-based systems
  • Neural networks