AI in Healthcare: Applications, Challenges, and Future Prospects

Abstract

The  widespread use of genetic information through next-generation sequencing technologies and the rapid growth of biomedical publications have ushered in the big data era in the field of cancer genomics.  Incorporating artificial intelligence (AI) techniques such as machine learning, deep learning, and natural language processing (NLP) into the process to address issues arising from high dimensionality and scalability of data and the transformation of large amounts of data into clinical data.  Practical knowledge is increasing and it is becoming the basis of precision medicine.  In this article, we review the current status and future topics of computational intelligence applications in disease genomics from the perspective of workflows to coordinate genomic investigations to improve treatment accuracy in malignant tumors. Essentially, existing  artificial intelligence systems and their failures in malignant growth genetic testing and diagnosis, such as variant calling and understanding, will be examined. Openly accessible devices and computations for important NLP advances in the search for evidence-based clinical implications are evaluated and discussed.  Furthermore, the present paper highlights the difficulties of accepting artificial intelligence  in computer-based health services in terms of information needs, algorithmic simplicity, reproducibility, and real-world evaluation, and highlights the difficulties of accepting artificial intelligence in computer-based health services, and Examines the importance of preparing patients and physicians for  medical services.  We expect simulated intelligence to continue to be a key driver of healthcare transformation to precision medicine. However, exceptional issues that arise need to be addressed to ensure health and positively impact health services.

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