Deep Learning in Medicine-Promise, Progress, and Challenges.

By Fei Wang, L. Casalino, Dhruv Khullar. JAMA Internal Medicine, 2019

422 citations recorded by the source corpus.

Subfields: Biomedical Engineering, General Health Professions, Health Informatics

View Paper: https://www.semanticscholar.org/paper/096d2678db2257177bbc8055a9ac051d181032b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Fei Wang

In General Health Professions

1. Fei Wang
2. Anita M. Preininger

In Health Informatics

1. Fei Wang
2. Kaveh Memarzadeh
3. Stefan Harrer
4. Adam Bohr
5. Anita M. Preininger

Citations in the corpus

• The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, neutral): “37”
• Artificial Intelligence for Clinical Trial Design. (2019, background): “First, the field of AI itself went through transformational advances, particularly in DL and related MLmethods, enabled by hardware improvements and very large training datasets [21,51].”
• Deep learning in generating radiology reports: A survey (2020, neutral): “Several recent surveys of DL applications [7,8] have been published in healthcare [9], electronic health records (EHR) [10], health informatics [11], medical image analysis [12,13], medicine [14,15], and even radiology [3,6,16,17].”
• AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “Moreover, to enhance the clinical utility of AI algorithms, they should be integrated into regular clinical workflows [119].”
• The rise of artificial intelligence in healthcare applications (2020, background): “There are various international efforts in gathering EMR data across countries including Observational Health Data Science and Informatics (OHDSI), who have consolidated 1.26 billion patient records from 17 different countries [40].”