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      Artificial Intelligence: How is It Changing Medical Sciences and Its Future?

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          Abstract

          Artificially intelligent computer systems are used extensively in medical sciences. Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. While computer systems often execute tasks more efficiently than humans, more recently, state-of-the-art computer algorithms have achieved accuracies which are at par with human experts in the field of medical sciences. Some speculate that it is only a matter of time before humans are completely replaced in certain roles within the medical sciences. The motivation of this article is to discuss the ways in which artificial intelligence is changing the landscape of medical science and to separate hype from reality.

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          Most cited references14

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          Dermatologist-level classification of skin cancer with deep neural networks

          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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            Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

            Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
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              Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel

              Abstract There is currently an outbreak of pneumonia of unknown aetiology in Wuhan, China. Although there are still several unanswered questions about this infection, we evaluate the potential for international dissemination of this disease via commercial air travel should the outbreak continue.

                Author and article information

                Journal
                Indian J Dermatol
                Indian J Dermatol
                IJD
                Indian Journal of Dermatology
                Wolters Kluwer - Medknow (India )
                0019-5154
                1998-3611
                Sep-Oct 2020
                : 65
                : 5
                : 365-370
                Affiliations
                [1] From the Covisus Inc, Monrovia, CA, USA
                [1 ] Adobe Research, San Jose, CA, USA
                [2 ] Whistle Labs, San Francisco, CA, USA
                [3 ] Department of Mathematics, Occidental College, Los Angeles, CA, USA
                Author notes
                Address for correspondence: Dr. Kanadpriya Basu, Covisus Inc, Monrovia, CA - 91016, USA. E-mail: kanad_basu@ 123456yahoo.com
                Article
                IJD-65-365
                10.4103/ijd.IJD_421_20
                7640807
                33165420
                52c4dea9-3f8f-4050-8e7f-8c67ddd69543
                Copyright: © 2020 Indian Journal of Dermatology

                This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

                History
                : May 2020
                : May 2020
                Categories
                Ijd® Symposium

                Dermatology
                artificial intelligence,deep convolutional neural network,medical use
                Dermatology
                artificial intelligence, deep convolutional neural network, medical use

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