Literature DB >> 30617339

High-performance medicine: the convergence of human and artificial intelligence.

Eric J Topol1.   

Abstract

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. 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.

Entities:  

Mesh:

Year:  2019        PMID: 30617339     DOI: 10.1038/s41591-018-0300-7

Source DB:  PubMed          Journal:  Nat Med        ISSN: 1078-8956            Impact factor:   53.440


  92 in total

1.  Eliminating waste in US health care.

Authors:  Donald M Berwick; Andrew D Hackbarth
Journal:  JAMA       Date:  2012-03-14       Impact factor: 56.272

2.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Authors:  Paras Lakhani; Baskaran Sundaram
Journal:  Radiology       Date:  2017-04-24       Impact factor: 11.105

3.  Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen.

Authors:  Jeffrey Alan Golden
Journal:  JAMA       Date:  2017-12-12       Impact factor: 56.272

4.  Not Just Digital Pathology, Intelligent Digital Pathology.

Authors:  Balazs Acs; David L Rimm
Journal:  JAMA Oncol       Date:  2018-03-01       Impact factor: 31.777

5.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Authors:  Babak Ehteshami Bejnordi; Mitko Veta; Paul Johannes van Diest; Bram van Ginneken; Nico Karssemeijer; Geert Litjens; Jeroen A W M van der Laak; Meyke Hermsen; Quirine F Manson; Maschenka Balkenhol; Oscar Geessink; Nikolaos Stathonikos; Marcory Crf van Dijk; Peter Bult; Francisco Beca; Andrew H Beck; Dayong Wang; Aditya Khosla; Rishab Gargeya; Humayun Irshad; Aoxiao Zhong; Qi Dou; Quanzheng Li; Hao Chen; Huang-Jing Lin; Pheng-Ann Heng; Christian Haß; Elia Bruni; Quincy Wong; Ugur Halici; Mustafa Ümit Öner; Rengul Cetin-Atalay; Matt Berseth; Vitali Khvatkov; Alexei Vylegzhanin; Oren Kraus; Muhammad Shaban; Nasir Rajpoot; Ruqayya Awan; Korsuk Sirinukunwattana; Talha Qaiser; Yee-Wah Tsang; David Tellez; Jonas Annuscheit; Peter Hufnagl; Mira Valkonen; Kimmo Kartasalo; Leena Latonen; Pekka Ruusuvuori; Kaisa Liimatainen; Shadi Albarqouni; Bharti Mungal; Ami George; Stefanie Demirci; Nassir Navab; Seiryo Watanabe; Shigeto Seno; Yoichi Takenaka; Hideo Matsuda; Hady Ahmady Phoulady; Vassili Kovalev; Alexander Kalinovsky; Vitali Liauchuk; Gloria Bueno; M Milagro Fernandez-Carrobles; Ismael Serrano; Oscar Deniz; Daniel Racoceanu; Rui Venâncio
Journal:  JAMA       Date:  2017-12-12       Impact factor: 56.272

6.  Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.

Authors:  Koichiro Yasaka; Hiroyuki Akai; Osamu Abe; Shigeru Kiryu
Journal:  Radiology       Date:  2017-10-23       Impact factor: 11.105

7.  The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.

Authors:  Takaya Saito; Marc Rehmsmeier
Journal:  PLoS One       Date:  2015-03-04       Impact factor: 3.240

8.  The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations.

Authors:  Hardeep Singh; Ashley N D Meyer; Eric J Thomas
Journal:  BMJ Qual Saf       Date:  2014-04-17       Impact factor: 7.035

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

Authors:  Kun-Hsing Yu; Ce Zhang; Gerald J Berry; Russ B Altman; Christopher Ré; Daniel L Rubin; Michael Snyder
Journal:  Nat Commun       Date:  2016-08-16       Impact factor: 14.919

10.  Deep learning in chest radiography: Detection of findings and presence of change.

Authors:  Ramandeep Singh; Mannudeep K Kalra; Chayanin Nitiwarangkul; John A Patti; Fatemeh Homayounieh; Atul Padole; Pooja Rao; Preetham Putha; Victorine V Muse; Amita Sharma; Subba R Digumarthy
Journal:  PLoS One       Date:  2018-10-04       Impact factor: 3.240

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  621 in total

1.  External validation and comparison of multiple prognostic scores in allogeneic hematopoietic stem cell transplantation.

Authors:  Roni Shouval; Joshua A Fein; Aniela Shouval; Ivetta Danylesko; Noga Shem-Tov; Maya Zlotnik; Ronit Yerushalmi; Avichai Shimoni; Arnon Nagler
Journal:  Blood Adv       Date:  2019-06-25

2.  Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.

Authors:  Valentina Bellemo; Gilbert Lim; Tyler Hyungtaek Rim; Gavin S W Tan; Carol Y Cheung; SriniVas Sadda; Ming-Guang He; Adnan Tufail; Mong Li Lee; Wynne Hsu; Daniel Shu Wei Ting
Journal:  Curr Diab Rep       Date:  2019-07-31       Impact factor: 4.810

3.  Improving Acute GI Bleeding Management Through Artificial Intelligence: Unnatural Selection?

Authors:  Neil Sengupta; David A Leiman
Journal:  Dig Dis Sci       Date:  2019-08       Impact factor: 3.199

4.  Artificial intelligence in intensive care: are we there yet?

Authors:  Matthieu Komorowski
Journal:  Intensive Care Med       Date:  2019-06-24       Impact factor: 17.440

5.  Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data.

Authors:  Andreas Holzinger; Benjamin Haibe-Kains; Igor Jurisica
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-15       Impact factor: 9.236

6.  deepDR: a network-based deep learning approach to in silico drug repositioning.

Authors:  Xiangxiang Zeng; Siyi Zhu; Xiangrong Liu; Yadi Zhou; Ruth Nussinov; Feixiong Cheng
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937

7.  ARTIFICIAL INTELLIGENCE FOR ENHANCING CLINICAL MEDICINE.

Authors:  Roxanna Daneshjou; Lukasz Kidzinski; Olga Afanasiev; Jonathan H Chen
Journal:  Pac Symp Biocomput       Date:  2020

Review 8.  Reinventing polysomnography in the age of precision medicine.

Authors:  Diane C Lim; Diego R Mazzotti; Kate Sutherland; Jesse W Mindel; Jinyoung Kim; Peter A Cistulli; Ulysses J Magalang; Allan I Pack; Philip de Chazal; Thomas Penzel
Journal:  Sleep Med Rev       Date:  2020-03-20       Impact factor: 11.609

9.  An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.

Authors:  Jae Ho Sohn; Yeshwant Reddy Chillakuru; Stanley Lee; Amie Y Lee; Tatiana Kelil; Christopher Paul Hess; Youngho Seo; Thienkhai Vu; Bonnie N Joe
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

10.  Adaptive Sedation Monitoring From EEG in ICU Patients With Online Learning.

Authors:  Wei-Long Zheng; Haoqi Sun; Oluwaseun Akeju; M Brandon Westover
Journal:  IEEE Trans Biomed Eng       Date:  2019-09-23       Impact factor: 4.538

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