Literature DB >> 31015651

Artificial intelligence in healthcare.

Kun-Hsing Yu1, Andrew L Beam1, Isaac S Kohane2,3.   

Abstract

Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

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Year:  2018        PMID： 31015651     DOI： 10.1038/s41551-018-0305-z

Source DB:  PubMed          Journal:  Nat Biomed Eng        ISSN： 2157-846X            Impact factor:   25.671


  240 in total

1.  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

Review 2.  Deep learning in histopathology: the path to the clinic.

Authors:  Jeroen van der Laak; Geert Litjens; Francesco Ciompi
Journal:  Nat Med       Date:  2021-05-14       Impact factor: 53.440

3.  Changing Health-Related Behaviors 6: Analysis, Interpretation, and Application of Big Data.

Authors:  Randy Giffen; Donald Bryant
Journal:  Methods Mol Biol       Date:  2021

4.  Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.

Authors:  Kun-Hsing Yu; Feiran Wang; Gerald J Berry; Christopher Ré; Russ B Altman; Michael Snyder; Isaac S Kohane
Journal:  J Am Med Inform Assoc       Date:  2020-05-01       Impact factor: 4.497

Review 5.  Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.

Authors:  Rodrigo Suarez-Ibarrola; Simon Hein; Gerd Reis; Christian Gratzke; Arkadiusz Miernik
Journal:  World J Urol       Date:  2019-11-05       Impact factor: 4.226

Review 6.  How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.

Authors:  Burak Kocak; Ece Ates Kus; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2020-10-01       Impact factor: 5.315

Review 7.  Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic.

Authors:  Julia L Marcus; Whitney C Sewell; Laura B Balzer; Douglas S Krakower
Journal:  Curr HIV/AIDS Rep       Date:  2020-06       Impact factor: 5.071

8.  Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.

Authors:  Francesca Coppola; Lorenzo Faggioni; Daniele Regge; Andrea Giovagnoni; Rita Golfieri; Corrado Bibbolino; Vittorio Miele; Emanuele Neri; Roberto Grassi
Journal:  Radiol Med       Date:  2020-04-29       Impact factor: 3.469

Review 9.  Antibacterial and Antiviral Functional Materials: Chemistry and Biological Activity toward Tackling COVID-19-like Pandemics.

Authors:  Bhuvaneshwari Balasubramaniam; Sudhir Ranjan; Mohit Saraf; Prasenjit Kar; Surya Pratap Singh; Vijay Kumar Thakur; Anand Singh; Raju Kumar Gupta
Journal:  ACS Pharmacol Transl Sci       Date:  2020-12-29

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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