Artificial Intelligence in Precision Medicine: Bridging the Gap with Engineering

Abstract

A lot of areas have been changed by artificial intelligence (AI), but precise medicine is one of the most important ones. AI has the ability to make personalized medical treatments much better by using machine learning (ML) and data analytics. This could lead to better results for patients. The goal of precision medicine is to make care plans that are specific to each person, taking into account things like genetics, surroundings, and way of life. However, there is still a gap between new AI innovations and real-world professional uses. Combining the latest gains in AI with cutting-edge tech solutions, such as genomics, medical imaging, and robots, is needed to close this gap. AI systems, like deep learning and natural language processing, have been used to handle huge amounts of genetic data. This has made it possible to find new biomarkers and disease paths. AI models that are combined with personal sensors and diagnostic tools also make it possible to keep an eye on patients in real time, which improves the accuracy of treatment even more. Microfluidic devices and automatic laboratory systems are examples of new technologies that have made it possible for AI-based solutions to work well in hospital settings. Even with these improvements, there are still big problems to solve, such as worries about data protection, the need for strong evaluation of AI models, and the need to connect these systems to the healthcare infrastructure that is already in place. In this study, the relationship between AI and engineering is looked at, with a focus on how they can work together to create the next generation of personalized medical solutions. We talk about ongoing research and suggest ways to get around the problems that are currently standing in the way. We stress how important it is for AI experts, healthcare workers, and engineers to work together to fully realize the promise of precision medicine.

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