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Disease infection prediction

Introduction

Machine Learning (ML) and Artificial Intelligence (AI) have become a major factor in the current world and are used in many different fields including healthcare. These technologies are changing the face of how healthcare practitioners think about and deal with diseases by means of predicting, preventing, diagnosing and managing them. Particularly on disease infection prediction, ML and AI have been found to be very effective. These technologies involve the use of algorithms that can learn from the data and perform tasks, and thus help the healthcare providers to have a better insight into the patterns and requirements of the people suffering from chronic diseases. This ability can result to better prediction and efficient interventions thus enhancing the patient results.

Challenges

There are various barriers in the implementation of ML and AI in disease infection prediction. The first challenge is that the healthcare data is usually siloed and structured in different databases and systems, thus creating a problem in collecting and integrating the data. The second challenge is privacy and security since patient information is sensitive. Thirdly, there is still a variation on how data is collected and analyzed thus affecting the accuracy of the predictions. In addition, more clinical studies are required in order to prove the efficiency of such tools. Also, there is a challenge of perceived and real resistance from the healthcare industry to change.

AI Solutions

There have been several challenges in implementing AI solutions for disease infection prediction despite the benefits of ML. For example, Google’s DeepMind has created an AI algorithm that can forecast the development of acute kidney injury as much as 48 hours ahead of time, which could reduce the number of deaths. Also, IBM Watson Health applies artificial intelligence in processing and integrating multiple data types to assess disease risk and treatment needs. PathAI is also another example, they have employed machine learning to enhance the disease diagnosis from the pathology slides. In addition, it is possible to create AI algorithms that can predict trends in disease spread which can be useful in strategizing and allocation of resources especially in epidemics.

Benefits

There are a number of ways in which the application of ML and AI can be advantageous in the field of disease infection prediction. Firstly, it can enhance the diagnostic capabilities and precision of the treatments. Secondly, it can also assist in forecasting diseases to advise preventive measures as well as early interjection. Thirdly, due to the analysis of big data, ML and AI are able to reveal new knowledge and innovations in the field of medicine. Lastly, it can also assist in the allocation of resources so that more than enough resources are available to healthcare givers in order to manage disease outbreaks.

Return on Investment

It is possible to achieve a good Return on Investment (ROI) when deploying ML and AI in disease infection prediction since the former can bring about positive changes in the healthcare sector. The following are some of the benefits that can be derived from enhanced speed and accuracy in diagnosis through these technologies; reduced healthcare costs and enhanced patient outcomes. Additionally, through outbreak prediction, they can assist in planning and allocation of resources which may in turn save lives and or resources. Additionally, the new knowledge that is provided by ML and AI can also contribute to the development of new cures and ways of preventing diseases which will in the long run help in cutting down the costs of healthcare.

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