Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.
Jia Xu,Pengwei Yang,Shang Xue,Bhuvan Sharma,Marta Sanchez-Martin,Fang Wang,Kirk A. Beaty,Dehan Elinor,Baiju Parikh +8 more
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TLDR
The current status and future directions of AI application in cancer genomics are reviewed within the context of workflows to integrate genomic analysis for precision cancer care and the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.Abstract:
In the field of cancer 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 cancer 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. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. 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.read more
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Key challenges for delivering clinical impact with artificial intelligence.
TL;DR: The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging, and robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy, is essential.
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Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
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TL;DR: This review aims to bring together these multidisciplinary and interdisciplinary features of MDR cancers by deciphering the molecular mechanisms underlying anticancer drug resistance, to pave the way towards the development of novel precision medicine treatment modalities that are able to surmount distinct and well-defined mechanisms of antic cancer drug resistance.
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Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives.
TL;DR: Examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers’ perspectives sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns.
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M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
Moe Elbadawi,Brais Muñiz Castro,Francesca K.H. Gavins,Jun Jie Ong,Simon Gaisford,Gilberto Pérez,Abdul Basit,Abdul Basit,Pedro Cabalar,Alvaro Goyanes,Alvaro Goyanes,Alvaro Goyanes +11 more
TL;DR: M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing, which includes producing filaments by hot melt extrusion (HME), using AI machine learning techniques (MLTs), and achieved high levels of accuracy by solely inputting the pharmaceutical excipients.
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