Publication Date: 2024/01/30
Abstract: This study focuses on the transformational nature of medicine through the prism of software engineering, AI, and ML. The study's overarching objectives were to determine the level of implementation and use of artificial intelligence and machine learning technologies in medical practice and a comprehensive study of their effectiveness in determining the results of health and drug production. It collected data from a mixed research method that used structured surveys to identify patterns of adoption, quantitative data obtained from the analysis of datasets, and qualitative data from semi-structured interviews administered to healthcare providers and software engineers. Quantitative analysis showed a clear trend toward using AI and ML, supported by empirical evidence indicating better diagnostic accuracy and personalized treatment recommendations. Qualitative insights helped to foster a cooperative spirit between health experts and software engineers, emphasizing the interdisciplinary nature of a victorious outcome. The study's ramifications go beyond health care and software engineering. It discusses the changes caused by artificial intelligence and machine-learning technologies. Furthermore, the conducted research points out the future research path, highlighting areas for improvement in the implementation process, the development of solid legal frameworks, and ethical considerations to ensure the advancement and improvement of artificial intelligence and machine learning adoption in the healthcare system.
Keywords: Software Engineering, Artificial Intelligence, Machine Learning, Healthcare Innovation, Drug Discovery.
DOI: https://doi.org/10.5281/zenodo.10648720
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN1199.pdf
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