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      Translation of Genomics into Precision Medicine with Artificial Intelligence: A Narrative Review on Applications, Challenges and Future Perspectives

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          Abstract

          In the field of genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence. (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

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          Author and article information

          Journal
          ScienceOpen Preprints
          ScienceOpen
          25 July 2020
          Affiliations
          [1 ] Doctor of Pharmacy, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Dr. MGR Medical University, Chennai.
          Article
          10.14293/S2199-1006.1.SOR-.PP75WQS.v1

          This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

          The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

          Medicine

          cancer care, digital healthcare, next generation sequencing, Artificial Intellingence, Precision Medicine, Natural language processing, Genomics

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