The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions, results, and current status of the patient’s conditions. Successively, we will be presenting a detailed survey for the medical XAI with the model enhancements, evaluation methods, significant overview of case studies with open box architecture, medical open datasets, and future improvements. Potential differences in AI and XAI methods are provided with the recent XAI methods stated as (i) local and global methods for preprocessing, (ii) knowledge base and distillation algorithms, and (iii) interpretable machine learning. XAI characteristics details with future healthcare explainability is included prominently, whereas the pre-requisite provides insights for the brainstorming sessions before beginning a medical XAI project. Practical case study determines the recent XAI progress leading to the advance developments within the medical field. Ultimately, this survey proposes critical ideas surrounding a user-in-the-loop approach, with an emphasis on human–machine collaboration, to better produce explainable solutions. The surrounding details of the XAI feedback system for human rating-based evaluation provides intelligible insights into a constructive method to produce human enforced explanation feedback. For a long time, XAI limitations of the ratings, scores and grading are present. Therefore, a novel XAI recommendation system and XAI scoring system are designed and approached from this work. Additionally, this paper encourages the importance of implementing explainable solutions into the high impact medical field.

The wood-based furniture manufacturing industries prioritize quality of production to meet higher market demands. Identifying various types of edge-glued wooden panel defects are a challenge for a human worker or a camera. Several studies have shown that the detection of edge-glued defects with low, high, normal, overlong, short is identified but detection of residue and bluntness is highly challenging. Thus, the present model identifies defects of low, high, normal, overlong, short by computer vision and/or deep learning, whereas defects of residue and bluntness by deep learning based decide by pass for having better performance. The goal of this paper is to provide an improved defect detection solution for wood-based furniture manufacturing industries by process automation. Therefore, a system was designed that takes defect input images from a camera as raw image and laser-aligned image for defect detection of the edge-glued wooden panel. The process automation then performs computer vision-based image features extraction with deep learning for defect detection. The aim of this paper is to solve edge-glued defect detection problems by using design and implementation of edge-glued wooden defect detection, that can be stated as edge-glued wooden panel defect detection using deep learning (WDD-DL) for process automation by artificial intelligence and Automated Optical Inspection (AOI) consolidation. Possibly there exist several types of defects on the edges while edge-banding on the wooden panel in furniture manufacturing. Therefore, the scope is to achieve higher accuracy by raw image and laser-aligned image feature extraction using deep learning algorithms for final result defect classification in WDD-DL by AOI. The WDD-DL system uses Gabor, Harris corner, morphology, structured light detection and curvature calculation for pre-processing and InceptionResnetV2 Convolutional Neural Network algorithm to attain the best results. The applications of this work can be found in quality control of the furniture manufacturing industry for an edge, corner, joint defect detection of the wooden panels. The WDD-DL achieves best results as the precision, recall and F1 score are 0.97, 0.90 and 0.92, respectively. The experiments demonstrate higher accuracy achievement as compared to other methods with overkill and escape rate analysis. Ultimately, the discussion section provides an interesting experience sharing about the necessary factors for implementing the WDD-DL in real-time industrial operations.

Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and public places. To detect parking lot occupancy in outdoor environments, street light control plays a crucial role in smart surveillance applications that can perform robustly in extreme surveillance environments. However, traditional parking occupancy systems are mostly implemented for outdoor environments using costly sensor-based techniques. This study uses the Jetson TX2 to develop a method that can accurately identify street parking occupancy and control streetlights to assist occupancy detection, thereby reducing costs, and can adapt to various weather conditions. The proposed method adopts You Only Look Once version 3 (YOLO v3, Seattle, WA, USA) based on MobileNet version 2 (MobileNet v2, Salt Lake City, UT, USA), which is area-based and uses voting to stably recognize occupancy status. This solution was verified using the CNRPark + EXT dataset, a simulated model, and real scenes photographed with a camera. Our experiments revealed that the proposed framework can achieve stable parking occupancy detection in streets.

Open-box models in medical domain have high acceptance and demand by many medical examiners. Even though the accuracy predicted by most of convolutional neural network (CNN) models is high, it is still not convincing as the detail discussion regarding the outcome is semi-transparent in the functioning process. As pneumonia is one of the top contagious infection that makes most of the population affected due to low immunity. Therefore, the goal of this paper is to implement an interpretable classification of pneumonia infection using eXplainable AI (XAI-ICP). Thus, XAI-ICP is the highly efficient system designed to solve this challenge by adapting to the recent population health conditions. The aim is to design an interpretable deep classification and transfer learning based evaluation for pneumonia infection classification. The model is primarily pre-trained using the open Chest X-Ray (CXR) dataset from National Institutes of Health (NIH). Whereas, the training input and testing given to this system is Taichung Veterans General Hospital (TCVGH) for independent learning, Taiwan + VinDr open dataset for transfer learning of pneumonia affected patients with labelled CXR images possessing three features of infiltrate, cardiomegaly and effusion. The data labelling is performed by the medical examiners with the XAI human-in-the-loop approach. The XAI-ICP demonstrates the XAI based re-configurable DCNN with human-in-the-loop as a novel approach. The interpretable deep classification provides detail transparency analysis and transfer learning for competitive accuracy. The purpose of this work, to design a re-configurable model that can continuously improve itself by using feedback system and provide feasibility for the model deployment across multiple countries to provide an efficient system for the pneumonia infection classification. The designed model then provides detail decisions taken at each step as transparency and features used within the algorithm for the pneumonia classification during the hospitalization. Thus, the scope can be given as explainable AI usage for the diagnosis classification using data preprocessing and interpretable deep convolutional neural network by the CXR evaluation. The accuracy achieved by using independent learning classification is 92.14% and is further improved based on successive transfer learning based evaluation is 93.29%. The XAI-ICP model adapts to the different population by using transfer learning, while providing competitive results to the affected conditions.

This study aimed to develop an early prediction model for identifying patients with bloodstream infections. The data resource was taken from 2015 to 2019 at Taichung Veterans General Hospital, and a total of 1647 bloodstream infection episodes and 3552 non-bloodstream infection episodes in the intensive care unit (ICU) were included in the model development and evaluation. During the data analysis, 30 clinical variables were selected, including patients’ basic characteristics, vital signs, laboratory data, and clinical information. Five machine learning algorithms were applied to examine the prediction model performance. The findings indicated that the area under the receiver operating characteristic curve (AUROC) of the prediction performance of the XGBoost model was 0.825 for the validation dataset and 0.821 for the testing dataset. The random forest model also presented higher values for the AUROC on the validation dataset and testing dataset, which were 0.855 and 0.851, respectively. The tree-based ensemble learning model enabled high detection ability for patients with bloodstream infections in the ICU. Additionally, the analysis of importance of features revealed that alkaline phosphatase (ALKP) and the period of the central venous catheter are the most important predictors for bloodstream infections. We further explored the relationship between features and the risk of bloodstream infection by using the Shapley Additive exPlanations (SHAP) visualized method. The results showed that a higher prothrombin time is more prominent in a bloodstream infection. Additionally, the impact of a lower platelet count and albumin was more prominent in a bloodstream infection. Our results provide additional clinical information for cut-off laboratory values to assist clinical decision-making in bloodstream infection diagnostics.

Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clinical parameters, but they lack predicting the recovery status after starting treatment. The goal of this paper is to provide a pneumonia status prediction system for the early affected patient’s discharge from the hospital within 7 days or late discharge more than 7 days. This paper aims to design a multimodal data analysis for pneumonia status prediction using deep learning classification (MDA-PSP). We have developed a system that takes an input of vital signs and CXR images of the affected patient with pneumonia from admission day 1 to day 3. The deep learning then classifies the health status improvement or deterioration for predicting the possible discharge state. Therefore, the scope is to provide a highly accurate prediction of the pneumonia recovery on the 7th day after 3-day treatment by the SHAP (SHapley Additive exPlanation), imputation, adaptive imputation-based preprocessing of the vital signs, and CXR image feature extraction using deep learning based on dense layers-batch normalization (BN) with class weights for the first 7 days’ general ward patient in MDA-PSP. A total of 3972 patients with pneumonia were enrolled by de-identification with an adult age of 71 mean ± 17 sd and 64% of them were male. After analyzing the data behavior, appropriate improvement measures are taken by data preprocessing and feature vectorization algorithm. The deep learning method of Dense-BN with SHAP features has an accuracy of 0.77 for vital signs, 0.92 for CXR, and 0.75 for the combined model with class weights. The MDA-PSP hybrid method-based experiments are proven to demonstrate higher prediction accuracy of 0.75 for pneumonia patient status. Henceforth, the hybrid methods of machine and deep learning for pneumonia patient discharge are concluded to be a better approach.