Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.

An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging

Purpose

To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements.

Design

Algorithm development for RNFL damage severity classification based on multicenter OCT data.

Subjects and Participants

A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models.

Methods

We developed an unsupervised k-means model to identify clusters of eyes with similar RNFL thickness profiles. We annotated the clusters based on their respective global RNFL thickness. We computed the optimal global RNFL thickness thresholds that discriminated different severity levels based on Bayes' minimum error principle. We validated the proposed pipeline based on an independent validation dataset with 2505 RNFL profiles from 1099 eyes of 900 subjects.

Main Outcome Measures

Accuracy, area under the receiver operating characteristic curve, and confusion matrix.

Results

The k-means clustering discovered 4 clusters with 1382, 1613, 1727, and 1839 samples with mean (standard deviation) global RNFL thickness of 58.3 (8.9) μm, 78.9 (6.7) μm, 87.7 (8.2) μm, and 101.5 (7.9) μm. The Bayes' minimum error classifier identified optimal global RNFL values of > 95 μ m , 86 to 95 μ m , 70 to 85 μ m , and < 70 μ m for discriminating normal eyes and eyes at the early, moderate, and advanced stages of RNFL thickness loss, respectively. About 4% of normal eyes and 98% of eyes with advanced RNFL loss had either global, or ≥ 1 quadrant, RNFL thickness outside of normal limits provided by the OCT instrument.

Conclusions

Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 μ m , 85 μm, and 70 μ m , respectively. This RNFL loss classification system is unbiased as there was no preassumption or human expert intervention in the development process. Additionally, it is objective, easy to use, and consistent, which may augment glaucoma research and day-to-day clinical practice.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning

Objective

To assess the performance of convolutional neural networks (CNNs) for automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic corneal deformation videos.

Design

Retrospective cohort study.

Participants

We retrospectively analyzed datasets with records of 734 nonconsecutive, refractive surgery candidates, and patients with unilateral or bilateral KC.

Methods

We first developed a video preprocessing pipeline to translate dynamic corneal deformation videos into 3-dimensional pseudoimage representations and then trained a CNN to directly identify KC from pseudoimages. We calculated the model's KC probability score cut-off and evaluated the performance by subjective and objective accuracy metrics using 2 independent datasets.

Main Outcome Measures

Area under the receiver operating characteristics curve (AUC), accuracy, specificity, sensitivity, and KC probability score.

Results

The model accuracy on the test subset was 0.89 with AUC of 0.94. Based on the external validation dataset, the AUC and accuracy of the CNN model for detecting KC were 0.93 and 0.88, respectively.

Conclusions

Our deep learning-based approach was highly sensitive and specific in separating normal from keratoconic eyes using dynamic corneal deformation videos at levels that may prove useful in clinical practice.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Artificial Intelligence in Pediatric Urology

Application of artificial intelligence (AI) is one of the hottest topics in medicine. Unlike traditional methods that rely heavily on statistical assumptions, machine learning algorithms can identify highly complex patterns from data, allowing robust predictions. There is an abundance of evidence of exponentially increasing pediatric urologic publications using AI methodology in recent years. While these studies show great promise for better understanding of disease and patient care, we should be realistic about the challenges arising from the nature of pediatric urologic conditions and practice, in order to continue to produce high-impact research.

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma

The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.

Surgical Artificial Intelligence: Endourology

Endourology is ripe with information that includes patient factors, laboratory tests, outcomes, and visual data, which is becoming increasingly complex to assess. Artificial intelligence (AI) has the potential to explore and define these relationships; however, humans might not be involved in the input, analysis, or even determining the methods of analysis. Herein, the authors present the current state of AI in endourology and highlight the need for urologists to share their proposed AI solutions for reproducibility outside of their institutions and prepare themselves to properly critique this new technology.

Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases

Numerous MRI-based artificial intelligence (AI) frameworks have been designed for prostate cancer lesion detection, segmentation, and classification via MRI as a result of intrareader and interreader variability that is inherent to traditional interpretation. Open-source data sets have been released with the intention of providing freely available MRIs for the testing of diverse AI frameworks in automated or semiautomated tasks. Here, an in-depth assessment of the performance of MRI-based AI frameworks for detecting, segmenting, and classifying prostate lesions using open-source databases was performed. Among 17 data sets, 12 were specific to prostate cancer detection/classification, with 52 studies meeting the inclusion criteria.

Bladder Cancer and Artificial Intelligence: Emerging Applications

Bladder cancer is a common and heterogeneous disease that poses a significant burden to the patient and health care system. Major unmet needs include effective early detection strategy, imprecision of risk stratification, and treatment-associated morbidities. The existing clinical paradigm is imprecise, which results in missed tumors, suboptimal therapy, and disease progression. Artificial intelligence holds immense potential to address many unmet needs in bladder cancer, including early detection, risk stratification, treatment planning, quality assessment, and outcome prediction. Despite recent advances, extensive work remains to affirm the efficacy of artificial intelligence as a decision-making tool for bladder cancer management.

Artificial Intelligence in Urology: Current Status and Future Perspectives

Surgical fields, especially urology, have shifted increasingly toward the use of artificial intelligence (AI). Advancements in AI have created massive improvements in diagnostics, outcome predictions, and robotic surgery. For robotic surgery to progress from assisting surgeons to eventually reaching autonomous procedures, there must be advancements in machine learning, natural language processing, and computer vision. Moreover, barriers such as data availability, interpretability of autonomous decision-making, Internet connection and security, and ethical concerns must be overcome.

Surgical Artificial Intelligence in Urology: Educational Applications

Surgical education has seen immense change recently. Increased demand for iterative evaluation of trainees from medical school to independent practice has led to the generation of an overwhelming amount of data related to an individual's competency. Artificial intelligence has been proposed as a solution to automate and standardize the ability of stakeholders to assess the technical and nontechnical abilities of a surgical trainee. In both the simulation and clinical environments, evidence supports the use of machine learning algorithms to both evaluate trainee skill and provide real-time and automated feedback, enabling a shortened learning curve for many key procedural skills and ensuring patient safety.

Genomics and Artificial Intelligence: Prostate Cancer

Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be missed by traditional methods. These AI-driven insights can lead to the discovery of novel biomarkers, enhance the accuracy of diagnosis, and predict disease progression and treatment response. As such, AI is becoming an indispensable tool in the pursuit of personalized medicine for prostate cancer.

Classification of produced water samples using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry

Produced water (PW) is a type of wastewater that arises during oil and gas production. Due to its potential environmental impact, PW is one of the most closely monitored forms of wastewater in the petroleum industry. The total oil and grease (TOG) content in the water is a crucial parameter for assessing the environmental impact of PW. Traditional methods for analyzing TOG in PW can be time-consuming and may not be compatible with green chemistry principles. In this study, an alternative method for classifying PW samples is proposed using a one-class classifier (OCC) model, which has proven useful for classification problems. To achieve this goal, headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography (GC×GC) were employed to obtain TOG profiles from PW. A series of simulated PW samples containing TOG were generated using a mixture design comprising four petrochemicals at concentrations ranging from 10 mg L-1 to 50 mg L-1. The polydimethylsiloxane (PDMS) fiber showed the most representative extraction of analytes. The optimization of the HS-SPME method was performed using a Doehlert design with two variables, and the final conditions were set at 80 °C and 70 min for extraction temperature and time, respectively. A pixel-based data approach was used to implement data-driven soft independent modeling by class analogy (DD-SIMCA). Although DD-SIMCA is a developing area in GC×GC studies, the proposed model produced outstanding results with a sensitivity of 94.3 %, specificity of 95.0 %, and accuracy of 94.5 %, considering the complex and broad compositional range of the modeled mixtures. These findings demonstrated the effectiveness of the OCC model approach in classifying PW samples according to environmental regulations.

Artificial Intelligence and Pathomics: Prostate Cancer

Artificial intelligence (AI) has the potential to transform pathologic diagnosis and cancer patient management as a predictive and prognostic biomarker. AI-based systems can be used to examine digitally scanned histopathology slides and differentiate benign from malignant cells and low from high grade. Deep learning models can analyze patient data from individual or multimodal combinations and identify patterns to be used to predict the response to different therapeutic options, the risk of recurrence or progression, and the prognosis of the newly diagnosed patient. AI-based models will improve treatment planning for patients with prostate cancer and improve the efficiency and cost-effectiveness of the pathology laboratory.

Radiomics and Artificial Intelligence: Renal Cell Carcinoma

There is a clinical need for accurate diagnosis and prognostication of kidney cancer using imaging. Radiomics and deep learning methods applied to imaging have shown promise in tasks such as tumor segmentation, classification, staging, and grading, as well as assessment of preoperative scores and correlation with tumor biomarkers. Artificial intelligence is also expected to play a significant role in advancing personalized medicine for the treatment of renal cell carcinoma.

Future Directions in the Treatment of Thyroid and Parathyroid Disease

The surgical management of thyroid and parathyroid disease has evolved considerably since the era of Theodor Kocher. We review the current trends in thyroid and parathyroid surgery concerning robotic surgery for remote access, the use of parathyroid autofluorescence detection technology to aid in the prevention of hypocalcemia as well as the use of thermal ablation to target thyroid nodules in a minimally invasive way. We also discuss how artificial intelligence is being used to improve the workflow and diagnostics preoperatively as well as for intraoperative decision-making. We also discuss potential areas where future research may enhance outcomes.

Synchrotron microtomography reveals insights into the degradation kinetics of bio-degradable coronary magnesium scaffolds

Bioresorbable magnesium scaffolds are a promising future treatment option for coronary artery stenosis, especially for young adults. Due to the degradation of these scaffolds (<1 year), long-term device-related clinical events could be reduced compared to treatments with conventional drug eluting stents. First clinical trials indicate a return of vasomotion after one year, which may be associated with improved long-term clinical outcomes. However, even after decades of development, the degradation process, ideal degradation time and biological response in vivo are still not fully understood. The present study investigates the in vivo degradation of magnesium scaffolds in the coronary arteries of pigs influenced by different strut thicknesses and the presence of antiproliferative drugs. Due to high 3D image contrast of synchrotron-based micro-CT with phase contrast (SR-μCT), a qualitative and quantitative evaluation of the degradation morphology of magnesium scaffolds was obtained. For the segmentation of the μCT images a convolutional network architecture (U-net) was exploited, demonstrating the huge potential of merging high resolution SR-μCT with deep learning (DL) supported data analysis. In total, 30 scaffolds, made of the rare earth alloy Resoloy®, with different strut designs were implanted into the coronary arteries of 10 domestic pigs for 28 days using drug-coated or uncoated angioplasty balloons for post-dilatation. The degradation morphology was analyzed using scanning electron microscopy, energy dispersive x-ray spectroscopy and SR-μCT. The data from these methods were then related to data from angiography, optical coherence tomography and histology. A thinner strut size (95 vs. 130 μm) and the presence of paclitaxel indicated a slower degradation rate at 28 d in vivo, which positively influences the late lumen loss (0.5 and 0.6 mm vs. 1.0 and 1.1 mm) and recoil values (0 and 1.7% vs. 6.1 and 22%).

The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics

The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution.

Rapid estimation of electroporation-dependent tissue properties in canine lung tumors using a deep neural network

The efficiency of electroporation treatments depends on the application of a critical electric field over the targeted tissue volume. Both the electric field and temperature distribution strongly depend on the tissue-specific electrical properties, which both differ between patients in healthy and malignant tissues and change in an electric field-dependent manner from the electroporation process itself. Therefore, tissue property estimations are paramount for treatment planning with electroporation therapies. Ex vivo methods to find electrical tissue properties often misrepresent the targeted tissue, especially when translating results to tumors. A voltage ramp is an in situ method that applies a series of increasing electric potentials across treatment electrodes and measures the resulting current. Here, we develop a robust deep neural network, trained on finite element model simulations, to directly predict tissue properties from a measured voltage ramp. There was minimal test error (R2>0.94;p<0.0001) in three important electric tissue properties. Further, our model was validated to correctly predict the complete dynamic conductivity curve in a previously characterized ex vivo liver model (R2>0.93;p<0.0001) within 100 s from probe insertion, showing great utility for a clinical application. Lastly, we characterize the first reported electrical tissue properties of lung tumors from five canine patients (R2>0.99;p<0.0001). We believe this platform can be incorporated prior to treatment to quickly ascertain patient-specific tissue properties required for electroporation treatment planning models or real-time treatment prediction algorithms. Further, this method can be used over traditional ex vivo methods for in situ tissue characterization with clinically relevant geometries.

Quantitative image analysis of microplastics in bottled water using artificial intelligence

The ubiquitous occurrence of microplastics (MPs) in the environment and the use of plastics in packaging materials result in the presence of MPs in the food chain and exposure of consumers. Yet, no fully validated analytical method is available for microplastic (MP) quantification, thereby preventing the reliable estimation of the level of exposure and, ultimately, the assessment of the food safety risks associated with MP contamination. In this study, a novel approach is presented that exploits interactive artificial intelligence tools to enable automation of MP analysis. An integrated method for the analysis of MPs in bottled water based on Nile Red staining and fluorescent microscopy was developed and validated, featuring a partial interrogation of the filter and a fully automated image processing workflow based on a Random Forest classifier, thereby boosting the analysis speed. The image analysis provided particle count, size and size distribution of the MPs. From these data, a rough estimation of the mass of the individual MPs, and consequently of the MP mass concentration in the sample, could be obtained as well. Critical materials, method performance characteristics, and final applicability were studied in detail. The method showed to be highly sensitive in sizing MPs down to 10 μm, with a particle count limit of detection and quantification of 28 and 85 items/500 mL, respectively. Linearity of mass concentration determined between 10 ppb and 1.5 ppm showed a regression coefficient (R2) of 0.99. Method precision was demonstrated by a repeatability of 9-16% RSD (n = 7) and within-laboratory reproducibility of 15-27% RSD (n = 21). Accuracy based on recovery was 92 ± 15% and 98 ± 23% at a level of 0.1 and 1.0 ppm, respectively. The quantitative performance characteristics thus obtained complied with regulatory requirements. Finally, the method was successfully applied to the analysis of twenty commercial samples of bottled water, with and without gas and flavor additives, yielding results ranging from values below the limit of detection to 7237 (95% CI [6456, 8088]) items/500 mL.

Accelerating COVID-19 Drug Discovery with High-Performance Computing

The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgency. Unfortunately, this is a scenario likely to repeat itself in future, so it is of interest to explore ways in which to accelerate drug discovery at pandemic speed. Computational methods naturally lend themselves to this because they can be performed rapidly if sufficient computational resources are available. Recently, high-performance computing (HPC) technologies have led to remarkable achievements in computational drug discovery and yielded a series of new platforms, algorithms, and workflows. The application of artificial intelligence (AI) and machine learning (ML) approaches is also a promising and relatively new avenue to revolutionize the drug design process and therefore reduce costs. In this review, I describe how molecular dynamics simulations (MD) were successfully integrated with ML and adapted to HPC to form a powerful tool to study inhibitors for four of the COVID-19 target proteins. The emphasis of this review is on the strategy that was used with an explanation of each of the steps in the accelerated drug discovery workflow. For specific technical details, the reader is directed to the relevant research publications.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Bedside Ultrasound to Identify and Predict Severity of Dysphagia Following Ischemic Stroke: Human Versus Artificial Intelligence

OBJECTIVE

Dysphagia is a significant ischemic stroke complication that can lead to aspiration. Identification of at-risk patients can be logistically difficult and costly. Researchers investigated whether quantitative ultrasound assessment of hyoid bone movement during induced swallowing would predict failure of videofluoroscopy (VFS) or fiberoptic endoscopic evaluation of swallowing (FEES), as determined by a penetration-aspiration scale (PAS) score. Additionally, ability of a machine learning (ML) algorithm to predict PAS success or failure from real-time ultrasound video recordings was assessed.

METHODS

A prospective, single-blinded, observational pilot study was conducted from June 2019 through March 2020 at a comprehensive stroke center on a convenience sample of patients admitted with diagnosis of acute ischemic stroke undergoing VFS or FEES as part of dysphagia assessment. Researchers performed a midsagittal airway ultrasound during swallowing in patients receiving an objective swallowing assessment by speech language pathologists who were blinded to ultrasound results. Sonologists measured hyoid bone movement, and researchers then constructed an ML algorithm designed for real-time video analysis using a long short-term memory network with an embedded VGG16 convolutional neural network.

RESULTS

Videos from 69 patients were obtained with their respective PAS results. In total, 90% of available videos were used for algorithm training. After training, the ML algorithm was challenged with the 10% previously unseen videos and then compared with PAS outcomes. Statistical analysis included logistic regression and correlation matrix testing on human ultrasound measurements. Cohen's κ was calculated to compare deep learning algorithm prediction with PAS results. Measurement of hyoid bone elevation, forward displacement, total displacement and mandible length was unable to predict PAS assessment outcome (p values = 0.36, 0.13, 0.11 and 0.32, respectively). The ML algorithm showed substantial agreement with PAS testing results for predicting test outcome (κ = 0.79; 95% confidence interval: 0.52-1.0) CONCLUSION: Manual ultrasound measurement of hyoid movement during swallowing in stroke patients failed to predict PAS swallowing results. However, an ML algorithm showed substantial agreement with PAS results despite a small data set, which could greatly improve access to dysphagia assessment in the future.

DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence

BACKGROUND AND OBJECTIVE

Artificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-like performance, but their use is limited since they are still largely viewed as opaque black boxes. This distrust remains the primary factor for their limited real application, particularly in healthcare. As a result, there is a need for interpretable predictors that provide better predictions and also explain their predictions.

METHODS

This study introduces "DeepXplainer", a new interpretable hybrid deep learning-based technique for detecting lung cancer and providing explanations of the predictions. This technique is based on a convolutional neural network and XGBoost. XGBoost is used for class label prediction after "DeepXplainer" has automatically learned the features of the input using its many convolutional layers. For providing explanations or explainability of the predictions, an explainable artificial intelligence method known as "SHAP" is implemented.

RESULTS

The open-source "Survey Lung Cancer" dataset was processed using this method. On multiple parameters, including accuracy, sensitivity, F1-score, etc., the proposed method outperformed the existing methods. The proposed method obtained an accuracy of 97.43%, a sensitivity of 98.71%, and an F1-score of 98.08. After the model has made predictions with this high degree of accuracy, each prediction is explained by implementing an explainable artificial intelligence method at both the local and global levels.

CONCLUSIONS

A deep learning-based classification model for lung cancer is proposed with three primary components: one for feature learning, another for classification, and a third for providing explanations for the predictions made by the proposed hybrid (ConvXGB) model. The proposed "DeepXplainer" has been evaluated using a variety of metrics, and the results demonstrate that it outperforms the current benchmarks. Providing explanations for the predictions, the proposed approach may help doctors in detecting and treating lung cancer patients more effectively.

Knowledge Graphs and Their Applications in Drug Discovery

Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate explainable predictions, which is a key topic in contemporary artificial intelligence. In this chapter, we outline some of the factors that need to be considered when constructing biomedical knowledge graphs, examine recent advances in mining such systems to gain insights for drug discovery, and identify potential future areas for further development.