期刊
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
卷 112, 期 -, 页码 68-73出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2017.12.003
关键词
Artificial intelligence in medicine; Natural language processing; Machine learning; Text analytics; Multidisciplinary teamwork; Cross-disciplinary research; Translational research
资金
- Philips Research
- National Institute of Bioimaging and Biomedical Engineering [EB017205-01A1]
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB017205] Funding Source: NIH RePORTER
Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multi-disciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies.
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