Artificial Intelligence in Translational Medicine
Simone Brogi,Vincenzo Calderone +1 more
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TLDR
In this paper, the most advanced applications of AI in translational medicine are analyzed, providing an up-to-date outlook regarding this emerging field, and the authors provide a review article.Abstract:
The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.read more
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Artificial Intelligence in Biological Sciences
TL;DR: The potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry are summarized.
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Drug discovery and development: introduction to the general public and patient groups
Natesh Singh,Philippe Vayer,Shivalika Tanwar,Jean-Luc Poyet,Katya Tsaioun,Bruno O. Villoutreix +5 more
TL;DR: In this paper , a pre-discovery stage is performed to understand the mechanisms leading to diseases and propose possible targets (e.g., proteins), followed by a preclinical development stage that focuses on clarifying the mode of action of the drug candidates, investigates potential toxicity, validates efficacy on various in vitro and in vivo models, and starts evaluate formulation, and finally, a clinical stage that investigates the drug candidate in humans, during which the drug is approved or not.
References
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Journal ArticleDOI
High-performance medicine: the convergence of human and artificial intelligence
TL;DR: Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
Journal ArticleDOI
A guide to deep learning in healthcare.
Andre Esteva,Alexandre Robicquet,Bharath Ramsundar,Volodymyr Kuleshov,Mark A. DePristo,Katherine Chou,Claire Cui,Greg S. Corrado,Sebastian Thrun,Jeffrey Dean +9 more
TL;DR: How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.
Journal ArticleDOI
Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.
TL;DR: The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.