{"id":973,"date":"2025-07-19T20:15:02","date_gmt":"2025-07-19T20:15:02","guid":{"rendered":"https:\/\/mediwhale.stage.studio-jt.co.kr\/kr\/?post_type=publication&#038;p=973"},"modified":"2025-07-19T20:18:43","modified_gmt":"2025-07-19T20:18:43","slug":"deep-learning-in-medicine-are-we-ready","status":"publish","type":"publication","link":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/publication\/deep-learning-in-medicine-are-we-ready\/","title":{"rendered":"Deep learning in medicine. Are we ready?"},"content":{"rendered":"\n<p>The real-world application of artificial intelligence (AI), machine learning (ML), and deep learning (DL), have generated significant interest throughout the computer science and medical communities in recent years. This interest has been accompanied by no small amount of hype. Though the term \u2018ML\u2019 was coined 50 years ago by Arthur Samuel, who stated that machines should have the ability to learn without being programmed,1\u00a0the advent of the graphics processing unit (GPU) has enabled much-improved processing power and enabled new possibilities with AI. DL\u2014an approach that utilizes multiple neural networks to learn a representation of data using multiple levels of abstraction2\u2014has revolutionized the computer vision field, and achieved substantial jumps in diagnostic performance for image recognition, speech recognition, and natural language processing.2\u00a0In the technical world, DL has been heavily used in autonomous vehicles,3\u00a0gaming4,5,\u00a0and numerous smartphone applications. The availability of different software (e.g. Caffe, Tensorflow), and the off-the-shelf convolutional neural networks (e.g. AlexNet, VGGNet, ResNet, and GoogleNet) have removed barriers to entry for many academics and clinicians, resulting in the recent surge of interest within the medical settings. To date, this technique has shown promising diagnostic performance, across specialties including ophthalmology (e.g. detection of diabetic retinopathy [DR], glaucoma, and age-related macular degeneration from fundus photographs and optical coherence tomographs),6-11\u00a0radiology (e.g. detection of tuberculosis from chest X-rays [CXRs], intracranial hemorrhage from computed tomography of the brain),12-15\u00a0and dermatology (e.g. detection of malignant melanoma from skin photographs).16<\/p>\n","protected":false},"template":"","publication_type_categories":[14],"publication_categories":[20],"class_list":["post-973","publication","type-publication","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication\/973","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/types\/publication"}],"version-history":[{"count":1,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication\/973\/revisions"}],"predecessor-version":[{"id":974,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication\/973\/revisions\/974"}],"wp:attachment":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/media?parent=973"}],"wp:term":[{"taxonomy":"publication_type_categories","embeddable":true,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication_type_categories?post=973"},{"taxonomy":"publication_categories","embeddable":true,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication_categories?post=973"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}