CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 052-054
DOI: 10.1055/s-0039-1677925
Special Section: Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications
Georg Thieme Verlag KG Stuttgart

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

Gretchen Jackson
1  IBM Watson Health, Cambridge, Massachusetts, USA
2  Vanderbilt University Medical Center, Nashville, Tennessee, USA
Jianying Hu
3  IBM Research, Yorktown Heights, New York, USA
Section Editors for the IMIA Yearbook Section on Artificial Intelligence in Health› Institutsangaben
Weitere Informationen


16. August 2019 (online)


Objective: To summarize significant research contributions to the field of artificial intelligence (AI) in health in 2018.

Methods: Ovid MEDLINE® and Web of Science® databases were searched to identify original research articles that were published in the English language during 2018 and presented advances in the science of AI applied in health. Queries employed Medical Subject Heading (MeSH®) terms and keywords representing AI methodologies and limited results to health applications. Section editors selected 15 best paper candidates that underwent peer review by internationally renowned domain experts. Final best papers were selected by the editorial board of the 2018 International Medical Informatics Association (IMIA) Yearbook.

Results: Database searches returned 1,480 unique publications. Best papers employed innovative AI techniques that incorporated domain knowledge or explored approaches to support distributed or federated learning. All top-ranked papers incorporated novel approaches to advance the science of AI in health and included rigorous evaluations of their methodologies.

Conclusions: Performance of state-of-the-art AI machine learning algorithms can be enhanced by approaches that employ a multidisciplinary biomedical informatics pipeline to incorporate domain knowledge and can overcome challenges such as sparse, missing, or inconsistent data. Innovative training heuristics and encryption techniques may support distributed learning with preservation of privacy.