Developing and Using Expert Systems and Neural Networks in Medicine: A Review on Benefits and Challenges
- 1.3k Downloads
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.
KeywordsArtificial intelligence Clinical decision making Expert systems Knowledge-based systems Neural networks
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.McLeod, R., and Schell, G. P., Management information systems. Pearson/Prentice Hall, USA, 2007.Google Scholar
- 2.Turban, J., Rainer, R., and Potter, R., Introduction to information technology. Wiley, New York, 2005.Google Scholar
- 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.CrossRefGoogle Scholar
- 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
- 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.CrossRefGoogle Scholar
- 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