Jia Xu|Pengwei Yang|Shang Xue|Bhuvan Sharma|Marta Sánchez-Martín|Fang Wang|Kirk Beaty|Elinor Dehan|Baiju Parikh
Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives
IBM (United States)|IBM (United States)|IBM (United States)|IBM (United States)|IBM (United States)|IBM (United States)|IBM (United States)|IBM (United States)|IBM (United States)
field cancer genomics broad availability genetic information offered nextgeneration sequencing technologies rapid growth biomedical publication led advent bigdata era integration artificial intelligence ai approaches machine learning deep learning natural language processing nlp tackle challenges scalability high dimensionality data transform big data clinically actionable knowledge expanding becoming foundation precision medicine paper review current status future directions ai application cancer genomics within context workflows integrate genomic analysis precision cancer care existing solutions ai limitations cancer genetic testing diagnostics variant calling interpretation critically analyzed publicly available tools algorithms key nlp technologies literature mining evidencebased clinical recommendations reviewed compared addition present paper highlights challenges ai adoption digital healthcare regard data requirements algorithmic transparency reproducibility realworld assessment discusses importance preparing patients physicians modern digitized healthcare believe ai remain main driver healthcare transformation toward precision medicine yet unprecedented challenges posed addressed ensure safety beneficial impact healthcare