Strategic Relevance of Artificial Intelligence: A Case Study on Image Recognition in the Medical Sector

Abstract

In order to discover how a medical company can model a Machine Vision application and communicate the performance findings to the CEO, this thesis has based its research on a case study of Højgaard Equine Hospital. The case study allowed the research to identify, how automation of a specific process could provide benefits to a medical company, including labour cost savings, mitigation of false negative diagnoses and increase the concistency of diagnoses across the industry. The identified process included detection of the disease OCD on the hock of horses. This process could be automated by use of image recognition (and therefore Machine Vision) as it involves a computer-aided detection task, where a pattern is discovered from looking through x-rays. In order to understand how to model this type of process, the research has applied concepts from Deep Learning, Machine Learning and Convolutional Neural Networks. The research analysis followed the CRISP-DM model in order to integrate data mining into the business of the case company. The specific business goal was to achieve a model performance above 0,50 (Recall) and a positive expected value. To overcome the issue with lack of data, different data augmentation techniques have been used to scale up the data to approximately 2600 unique x-rays. The training and testing of model 1 have been executed on the IBM Watson platform by use of a pre-trained CNN, where only the final layer has been trained. The performance of model 1 includes a Recall of 0,47 and an expected profit loss of -60,45 DKK for the Discrete Classifier and a Recall of 0,75 and max profit of 170,72 DKK for the Ranking Classifier. The performance of model 2 includes a Recall of 0,66 and an expected profit of 173,28 DKK for the Discrete Classifier and a Recall of 0,85 and max profit of 211,74 DKK for the Ranking Classifier. The performance of model 2 is superior to model 1 and model 2 fulfills the business goal. The performance of the models can be communicated to the CEO by use of profit curves and ROC graphs, which are useful metrics for business decision-making. The deployment of the model is based on a 4-step approach, which takes into account the associated risk & maturity when integrating the model into the existing business landscape. The recommendation is to use the model for decision support. Reflections on the research process produced findings that are relevant to implement for a future research, i.e. more frequent meetings with domain experts as well as starting the data collection much earlier in the research process. Reflections on ethical issues suggested that humans should not prevent automation, but accept it, and direct their focus towards more value-adding activities. The findings of this research contribute to the research field of image recognition applied within the medical sector as they explain how to model and communicate two models that are based on convolutional neural networks.