Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications
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.
- 1 Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25 (01) 44-56
- 2 Lamy JB, Seroussi B, Griffon N, Kerdelhue G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (02) 135-44
- 3 Albers DJ, Levine ME, Stuart A, Mamykina L, Gluckman B, Hripcsak G. Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. J Am Med Inform Assoc 2018; 25 (10) 1392-401
- 4 Belciug S, Gorunescu F. Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection. J Biomed Inform 2018; 83: 159-66
- 5 Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A. , et al. Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc 2018; 25 (08) 945-54
- 6 Gruetzemacher R, Gupta A, Paradice D. 3D deep learning for detecting pulmonary nodules in CT scans. J Am Med Inform Assoc 2018; 25 (10) 1301-10
- 7 Hassan T, Akram MU, Akhtar M, Khan SA, Yasin U. Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula. J Med Syst 2018; 42 (11) 17
- 8 Lee J, Sun JM, Wang F, Wang S, Jun CH, Jiang XQ. Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis. JMIR Med Inf 2018; 6 (02) 4-24
- 9 Li XR, Zhu DX, Levy P. Leveraging auxiliary measures: a deep multi-task neural network for predictive modeling in clinical research. BMC Med Inform Decis Mak 2018; 18: 9
- 10 Liu MX, Zhang J, Nie D, Yap PT, Shen DG. Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis. IEEE J Biomed Health Inform 2018; 22 (05) 1476-85
- 11 Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J. , et al. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Trans Med Imaging 2018; 37 (02) 384-95
- 12 Powers S, Qian JY, Jung K, Schuler A, Shah NH, Hastie T. , et al. Some methods for heterogeneous treatment effect estimation in high dimensions. Stat Med 2018; 37 (11) 1767-87
- 13 Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. IEEE Trans Med Imaging 2018; 37 (02) 491-503
- 14 Shi J, Zheng X, Li Y, Zhang Q, Ying SH. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer’s Disease. IEEE J Biomed Health Inform 2018; 22 (01) 173-83
- 15 Uppu S, Krishna A. A deep hybrid model to detect multi-locus interacting SNPs in the presence of noise. Int J Med Inf 2018; 119: 134-51
- 16 Xiao YW, Wu J, Lin ZL, Zhao XD. A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data. Comput Meth Programs Biomed 2018; 166: 99-105
- 17 Luo Y, Cheng Y, Uzuner O, Szolovits P, Starren J. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes. J Am Med Inform Assoc 2018; 25 (01) 93-8
- 18 Wright Jr JT, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV. , et al. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med 2015; 373 (22) 2103-16