The Use of AI and Machine Learning in Healthcare and its Potential to Improve Patient Outcomes

Abstract

The use of artificial intelligence (AI) and machine learning (ML) in healthcare has the potential to revolutionize the way in which patients are diagnosed, treated, and monitored. The ability of AI and ML algorithms to process and analyse large amounts of data has led to the development of new diagnostic and treatment tools that can improve patient outcomes. However, the use of these technologies in healthcare is still in its infancy, and there is a need for further research to fully understand their potential impact. Recent studies have shown that AI can improve diagnostic accuracy in a variety of medical fields, including radiology, pathology, and dermatology (Hashmi, 2017; Schüffler, 2016). In radiology, for example, deep learning algorithms have been used to analyse medical images, such as mammograms and CT scans, with a level of accuracy that is comparable to that of human radiologists (Yang, 2018). In pathology, AI algorithms have been used to analyse medical images, such as biopsy slides, with a level of accuracy that is comparable to that of human pathologists (Thrall, 2018). Furthermore, AI and ML have the potential to improve patient outcomes by identifying high-risk patients and providing personalized treatment plans. For example, machine learning algorithms have been used to predict the risk of readmission in patients with heart failure (Murphy, 2020). This can help to identify patients who are at high risk for readmission and provide them with targeted interventions to prevent readmission. Despite the potential benefits of AI and ML in healthcare, there are also potential challenges and limitations that need to be considered. These include issues related to data privacy and security, as well as concerns about the potential impact of these technologies on healthcare workforce (Hashmi, 2017). In conclusion, the use of AI and ML in healthcare has the potential to revolutionize the way in which patients are diagnosed, treated, and monitored. However, further research is needed to fully understand the potential impact of these technologies on patient outcomes and to address potential challenges and limitations.