It is acknowledged that the appropriate use of the neural network-based methodologies in medical problem solving could be very effective to improve the efficiency and the quality of medical care. The growing number of projects that employ neural network based methodologies in medical care makes necessary to examine the level and the role that neural networks will play in the development of automatic diagnostic systems. This paper considers the use of neural networks in image processing and knowledge representation in image interpretation systems from an ethical perspective. The diagnostic attributes of such systems are based on the result of the feature extraction procedure from digital images, in conjunction with previously available medical knowledge, in order to expand this knowledge. A key issue in the use of neural network methods in a medical application of this kind is that it is unclear how decisions are reached.
Most neural networks suffer from the opaqueness of their learned associations.
In medical applications, this black box nature may make clinicians reluctant to utilise a neural network application, no matter how great the claims made for its performance. Thus, there is need to enhance neural networks with rule extraction capabilities. In addition, it is necessary to examine how productivity can be increased and how quality can be assured. This examination addresses the specification of the problem, the development of appropriate representations for the network input and output information and the preparation of the training, testing and validation data. Using the acquisition, processing, storage, dissemination and use stages of an information life cycle as a basis for the different stages for the development and use of neural network medical imaging applications, this paper elaborates on the broader ethical implications of the use of neural applications in medicine.