Table 5. Artificial Intelligence studies related to lung pathology.

Reference	Objective	AI algorithm	Application	Main results
Yu KH, et al.	To improve the prognostic prediction of lung adenocarcinoma and squamous cell carcinoma patients through objective features distilled from histopathology images	Elastic net-Cox proportional hazards model	Prediction of the prognosis of lung cancer by automated pathology image features and thereby contribution to precision oncology	Automatically derived image features can predict the prognosis of lung cancer patients
Coudray N, et al.	To train a deep convolutional neural network on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them	Deep convolutional neural network	Detection of cancer subtype or gene mutations and mutation prediction from non-small cell lung cancer histopathology	Deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations
Wei JW, et al.	To propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides	Deep neural network	Improvement of classification of lung adenocarcinoma patterns	All evaluation metrics for the model and the three pathologists were within 95% confidence intervals of agreement
Gertych A, et al.	To a pipeline equipped with a CNN to distinguish four growth patterns of pulmonary adenocarcinoma (acinar, micropapillary, solid, and cribriform) and separate tumor regions from non-tumor	Convolutional neural network	To assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review	The overall accuracy of distinguishing the tissue classes was 89.24%
KanavatI F, et al.	To train a CNN, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images	Convolutional neural network	Development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists	Highly promising results for differentiating between lung carcinoma and non-neoplastic lesion

CNN, Convolutional Neural Network.