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      New Roles for Clinicians in the Age of Artificial Intelligence

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          Abstract

          With the rapid developments of digital picture processing, pattern recognition, and intelligent algorithms, artificial intelligence (AI) has been widely applied in the medical field. The applications of artificial intelligence in medicine (AIM) include diagnosis generation, therapy selection, healthcare management, disease stratification, etc. Among the applications, the focuses of AIM are assisting clinicians in implementing disease detection, quantitative measurement, and differential diagnosis to improve diagnostic accuracy and optimize treatment selection. Thus, researchers focus on creating and refining modeling processes, including the processes of data collection, data preprocessing, and data partitioning as well as how models are configured, evaluated, optimized, clinically applied, and used for training. However, there is little research on the consideration of clinicians in the age of AI. Meanwhile, AI is more accurate and spends less time in diagnosis between the competitions of AI and clinicians in some cases. Thus, AIM is gradually becoming a hot topic. Barely a day goes by without a claim that AI techniques are poised to replace most of today’s professionals. Despite huge promise surrounding this technology, AI alone cannot support all the requirements for precision medicine, rather AI should be used in cohesive collaboration with clinicians. However, the integration of AIM has created confusion among clinicians on their role in this era. Therefore, it is necessary to explore new roles for clinicians in the age of AI.

          Statement of significance

          With the advent of the era of AI, the integration of medical field and AI is on the rise. Medicine has undergone significant changes, and what was previously labor-intensive work is now being solved through intelligent means. This change has also raised concerns among scholars: Will doctors eventually be replaced by AI? From this perspective, this study elaborates on the reasons why AI cannot replace doctors, and points out how doctors should change their roles to accelerate the integration of these fields, so as to adapt to the developing times.

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          Most cited references 14

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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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            Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

            Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.
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              Deep Learning in Medical Image Analysis

              This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
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                Author and article information

                Journal
                BIOI
                BIO Integration
                BIOI
                Compuscript (Ireland )
                2712-0082
                2712-0074
                01 December 2020
                17 September 2020
                : 1
                : 3
                : 113-117
                Affiliations
                1Department of Ultrasound Medicine, Laboratory of Ultrasound Molecular Imaging, The Third Affiliated Hospital of Guangzhou Medical University of Guangzhou Medical University, The Liwan Hospital of the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China
                Author notes
                Correspondence to: Zhiyi Chen Tel: +86-020-81292115 E-mail: zhiyi_chen@ 123456gzhmu.edu.cn
                Article
                bioi20200014
                10.15212/bioi-2020-0014
                Copyright © 2020 The Authors

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See https://bio-integration.org/copyright-and-permissions/

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                Self URI (journal-page): https://bio-integration.org/
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