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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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X-Ray image analysis

Introduction

AI has continued to advance in many fields and healthcare is no different. Another important application where AI has been proven to be very effective is in the analysis of X-Ray images, an activity that has been done mainly by radiologists. With Computer Vision (CV), an AI technology that enables the machines to ‘observe’ and ‘understand’ the visuals, the task of assessing X-Ray images can be done automatically and may help to improve the outcomes. The application of CV AI in the analysis of X-Ray images is a great potential to transform the healthcare sector; however, it has several issues that require consideration.

Challenges

There are certain barriers that can limit the effectiveness of implementing CV AI in X-Ray image analysis. The first challenge that can be identified is that there is a large number of X-Ray images which make it hard for the algorithms to provide accurate results. Also, such images can be of different quality, and thus AI cannot be relied upon to make right decisions all the time. Another challenge is data privacy since medical images are considered as personal information. Last but not the least, there is the issue of trust. Both the patients and the healthcare practitioners have to trust the AI analysis and this is not easily achievable especially where the reasons for the AI’s decisions are not well understood.

AI Solutions

There are however several challenges that hinder the use of X-Ray images in AI such as; Despite these challenges, numerous AI solutions have been designed to enhance the performance of detecting and analyzing X-Ray images. For instance, the global technological power, Google’s DeepMind has created an AI system that has been found to be better than many radiologists in identifying specific types of lung cancer from X-Ray images. Another AI solution which is widely used is Zebra Medical Vision that applies machine learning techniques to identify a number of diseases through X-Ray images including lung cancer, cardiovascular diseases, and liver diseases. In the aspect of data privacy, it is possible to apply federated learning and differential privacy to ensure that the sensitive patient information is not exposed. To this end, an effort is being made to make the AI decisions more clear, for instance by providing visualizations and simple explanations.

Benefits

There are numerous advantages of employing CV AI in X-Ray image analysis. Thus, AI can help to solve a significant problem of radiologists – the lack of time, helping the professionals to concentrate on the difficult cases only. In addition, AI has the capacity of identifying diseases at an early stage and with higher level of accuracy as compared to the human beings. They can also decrease the health care expenses because they can avoid ordering of unnecessary tests and treatments. Lastly, it can act as a good consultant to the existing system to reduce cases of misdiagnosis and provide the best services to patients.

Return on Investment

There is a huge initial investment when it comes to integrating AI but the returns can be immense. The following are the potential areas of saving through AI; increased efficiency, reduced errors and improved patient outcomes. For instance, a research done by Accenture revealed that AI applications have the potential of reducing costs in the U. S healthcare sector by $150 billion by 2026. In addition, by releasing the radiologists to deal with other issues that require their expertise, AI would also help to increase their efficiency and happiness, thus leading to more benefits.

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