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Artificial Intelligence in the Pharmaceutical Industry - An Overview of Innovations

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Ayn serves as AI Analyst at Emerj - covering artificial intelligence use-cases and trends across industries. She previously held various roles at Accenture. Several factors have contributed to the advancement of AI in the pharmaceutical industry. These factors include the increase in the size of and the greater variety of types of biomedical datasets, as a result of the increased usage of electronic health records. This article intends to provide business leaders in the pharmacy space with an idea of what they can currently expect from Ai in their industry.


Medical Report Generation Using Deep Learning

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Image Captioning is a challenging artificial intelligence problem which refers to the process of generating textual description from an image based on the image contents. A common answer would be "A woman playing a guitar". We as humans can look at a picture and describe whatever it is in it, in an appropriate language. For all of us'non-radiologists', a common answer would be "a chest x-ray". Well, we are not wrong but a radiologist might have some different interpretations.


Taking Matters into Your Own Hands

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See also the article by Pan et al in this issue. Safwan S. Halabi, MD, is a clinical associate professor of radiology at the Stanford University School of Medicine and serves as the medical director for radiology informatics at Stanford Children's Health. Dr Halabi's clinical and administrative leadership roles are directed at improving quality of care, efficiency, and patient safety. His current academic and research interests include imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support, and patient-centric health care delivery. Bone age assessment became an early AI "poster child" that demonstrated the power of applying regression and machine learning techniques to a mundane and monotonous radiologic diagnostic task.


Accelerating Brain Mapping with AI and HPC

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In the development of artificial intelligence applications, the holy grail is the creation of an artificial neural network that functions like the human brain. This is an elusive goal, because the human brain is an extremely complex organ that functions in flexible and fluid ways that can be difficult to replicate in the world of AI. Today, a team of leading-edge scientific researchers are making breakthroughs in this area by using functional magnetic resonance imaging (fMRI) of the brains of people carrying out various cognitive tasks. The goal is to better understand and create computational models of how the brain works, and then use those models to train artificial neural networks to map images to actions quickly and accurately. For example, having a fully developed computational model of how memory works would make it possible to compare brain activity and to understand which model is playing out in the simulated brain of a patient.



Convolutional Neural Networks for Medical Images Diagnosis

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This course was designed and prepared to be a practical CNN-based medical diagnosis application. It focuses on understanding by examples how CNN layers are working, how to train and evaluate CNN, how to improve CNN performances, how to visualize CNN layers, and how to deploy the final trained CNN model. All the development tools and materials required for this course are FREE. Besides that, all implemented Python codes are attached with this course.Who this course is for: Dr. Hussein received his B.Eng. degree in Computer Engineering (2006 Yarmouk University, Jordan), M.Eng.


Deep learning-based classification of resting-state fMRI independent-component analysis

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Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here, we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants respectively.


The human brain built by AI: A transatlantic collaboration

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The Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) is a collaboration between McGill University and Forschungszentrum Jülich to develop next-generation high-resolution human brain models using cutting-edge Machine- and Deep Learning methods and high-performance computing. HIBALL is based on the high-resolution BigBrain model first published by the Jülich and McGill teams in 2013. Over the next five years, the lab will be funded with a total of up to 6 million Euro by the German Helmholtz Association, Forschungszentrum Jülich, and Healthy Brains, Healthy Lives at McGill University. In 2003, when Jülich neuroscientist Katrin Amunts and her Canadian colleague Alan Evans began scanning 7,404 histological sections of a human brain, it was completely unclear whether it would ever be possible to reconstruct this brain on the computer in three dimensions. At that time, there were no technical possibilities to cope with the huge amount of data.


Future of AI Part 5: The Cutting Edge of AI

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Edmond de Belamy is a Generative Adversarial Network portrait painting constructed in 2018 by Paris-based arts-collective Obvious and sold for $432,500 in Southebys in October 2018.


Deep Learning to Automate Reference-Free Image Quality Assessment of Whole-Heart MR Images

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To develop and characterize an algorithm that mimics human expert visual assessment to quantitatively determine the quality of three-dimensional (3D) whole-heart MR images. In this study, 3D whole-heart cardiac MRI scans from 424 participants (average age, 57 years 18 [standard deviation]; 66.5% men) were used to generate an image quality assessment algorithm. A deep convolutional neural network for image quality assessment (IQ-DCNN) was designed, trained, optimized, and cross-validated on a clinical database of 324 (training set) scans. On a separate test set (100 scans), two hypotheses were tested: (a) that the algorithm can assess image quality in concordance with human expert assessment as assessed by human-machine correlation and intra- and interobserver agreement and (b) that the IQ-DCNN algorithm may be used to monitor a compressed sensing reconstruction process where image quality progressively improves. Weighted κ values, agreement and disagreement counts, and Krippendorff α reliability coefficients were reported.