Waheed S, Tahir MJ, Ullah I, Alwalid O, Irshad SG, Asghar MS, et al. The impact of dependence on advanced imaging techniques on the current radiology practice. Ann Med Surg (Lond). 2022;78:103708.

PubMed  Google Scholar 

Rego J, Tan K. Advances in imaging-the changing environment for the imaging specialist. Perm J. 2006;10(1):26–8.

PubMed  PubMed Central  Google Scholar 

Brady A, Brink J, Slavotinek J. Radiology and value-based health care. JAMA. 2020;324(13):1286–7.

PubMed  Google Scholar 

Vliegenthart R, Fouras A, Jacobs C, Papanikolaou N. Innovations in thoracic imaging: CT, radiomics. AI and x-ray velocimetry Respirology. 2022;27(10):818–33.

PubMed  Google Scholar 

Gulati A, Balasubramanya R. Lung imaging. StatPearls. Treasure Island (FL): StatPearls Publishing; 2023.

Jin D, Harrison AP, Zhang L, Yan K, Wang Y, Cai J, et al. Artificial intelligence in radiology. Artificial intelligence in medicine. Elsevier. 2021;265–89.

Langlotz CP. Will artificial intelligence replace radiologists? Radiol Artif Intell. 2019;1(3): e190058.

PubMed  PubMed Central  Google Scholar 

van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021.

AlNuaimi D, AlKetbi R. The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic. BJR Open. 2022;4(1):20210075.

PubMed  PubMed Central  Google Scholar 

Tandon YK, Bartholmai BJ, Koo CW. Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules. J Thorac Dis. 2020;12(11):6954–65.

PubMed  PubMed Central  Google Scholar 

Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SBM, Savoldelli M, et al. Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries. Global Health. 2020;16(1):52.

PubMed  PubMed Central  Google Scholar 

McAdams HP, Samei E, Dobbins J, Tourassi GD, Ravin CE. Recent advances in chest radiography. Radiology. 2006;241(3):663–83.

PubMed  Google Scholar 

Barta JA, Powell CA, Wisnivesky JP. Global epidemiology of lung cancer. Ann Glob Health. 2019;85(1).

National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395–9.

Precious BJ, Raju R, Leipsic J. Recent advances in thoracic x-ray computed tomography for pulmonary imaging. Can Respir J. 2014;21(5):307–9.

PubMed  PubMed Central  Google Scholar 

Hatabu H, Ohno Y, Gefter WB, Parraga G, Madore B, Lee KS, et al. Expanding applications of pulmonary MRI in the clinical evaluation of lung disorders: Fleischner Society position paper. Radiology. 2020;297(2):286–301.

PubMed  Google Scholar 

•• Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–10. The article highlights the importance of AI in disease diagnosis, treatment, and response assessment to improve patient outcomes.

CAS  PubMed  PubMed Central  Google Scholar 

Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol. 2019;25(6):485–95.

PubMed  PubMed Central  Google Scholar 

Becker J, Decker JA, Römmele C, Kahn M, Messmann H, Wehler M, et al. Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs. Diagnostics (Basel). 2022;12(6).

Qin ZZ, Sander MS, Rai B, Titahong CN, Sudrungrot S, Laah SN, et al. Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep. 2019;9(1):15000.

PubMed  PubMed Central  Google Scholar 

Causey JL, Zhang J, Ma S, Jiang B, Qualls JA, Politte DG, et al. Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep. 2018;8(1):9286.

PubMed  PubMed Central  Google Scholar 

Ahn JS, Ebrahimian S, McDermott S, Lee S, Naccarato L, Di Capua JF, et al. Association of artificial intelligence-aided chest radiograph interpretation with reader performance and efficiency. JAMA Netw Open. 2022;5(8): e2229289.

PubMed  PubMed Central  Google Scholar 

Li D, Wang D, Dong J, Wang N, Huang H, Xu H, et al. False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: role of deep-learning-based CT diagnosis and insights from two cases. Korean J Radiol. 2020;21(4):505–8.

PubMed  PubMed Central  Google Scholar 

Kundu R, Das R, Geem ZW, Han G-T, Sarkar R. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLoS ONE. 2021;16(9): e0256630.

CAS  PubMed  PubMed Central  Google Scholar 

Alshamrani K, Alshamrani HA, Asiri AA, Alqahtani FF, Mohammad WT, Alshehri AH. The use of chest radiographs and machine learning model for the rapid detection of pneumonitis in pediatric. Biomed Res Int. 2022;21(2022):5260231.

Google Scholar 

Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, et al. The applications of artificial intelligence in chest imaging of COVID-19 patients: a literature review. Diagnostics (Basel). 2021;11(8).

•• Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635–40. The study aimed to show that deep learning with X-ray imaging could be a potential method for COVID-19 pneumonia diagnosis.

PubMed  PubMed Central  Google Scholar 

Li MD, Arun NT, Gidwani M, Chang K, Deng F, Little BP, et al. Automated assessment and tracking of COVID-19 pulmonary disease severity on chest radiographs using convolutional siamese neural networks. Radiol Artif Intell. 2020;2(4): e200079.

PubMed  PubMed Central  Google Scholar 

Zhu J, Shen B, Abbasi A, Hoshmand-Kochi M, Li H, Duong TQ. Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS ONE. 2020;15(7): e0236621.

CAS  PubMed  PubMed Central  Google Scholar 

Islam R, Tarique M. Chest X-ray images to differentiate COVID-19 from pneumonia with artificial intelligence techniques. Int J Biomed Imaging. 2022;22(2022):5318447.

Google Scholar 

• Ko H, Chung H, Kang WS, Kim KW, Shin Y, Kang SJ, et al. COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: model development and validation. J Med Internet Res. 2020;22(6): e19569. The authors describe the development of an AI diagnostic tool with a high diagnostic performance.

PubMed  PubMed Central  Google Scholar 

İn E, Geçkil AA, Kavuran G, Şahin M, Berber NK, Kuluöztürk M. Using artificial intelligence to improve the diagnostic efficiency of pulmonologists in differentiating COVID-19 pneumonia from community-acquired pneumonia. J Med Virol. 2022;94(8):3698–705.

PubMed  PubMed Central  Google Scholar 

Zhang H-T, Zhang J-S, Zhang H-H, Nan Y-D, Zhao Y, Fu E-Q, et al. Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. Eur J Nucl Med Mol Imaging. 2020;47(11):2525–32.

CAS  PubMed  PubMed Central  Google Scholar 

Global Tuberculosis Report 2022 [Internet]. [cited 2023 Mar 30]. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022

Acharya V, Dhiman G, Prakasha K, Bahadur P, Choraria AMS, et al. AI-assisted tuberculosis detection and classification from chest X-rays using a deep learning normalization-free network model. Comput Intell Neurosci. 2022;2022:2399428.

Liao Q, Feng H, Li Y, Lai X, Pan J, Zhou F, et al. Evaluation of an artificial intelligence (AI) system to detect tuberculosis on chest X-ray at a pilot active screening project in Guangdong, China in 2019. J Xray Sci Technol. 2022;30(2):221–30.

PubMed  PubMed Central  Google Scholar 

Nachiappan AC, Rahbar K, Shi X, Guy ES, Mortani Barbosa EJ, Shroff GS, et al. Pulmonary tuberculosis: role of radiology in diagnosis and management. Radiographics. 2017;37(1):52–72.

PubMed  Google Scholar 

Yan C, Wang L, Lin J, Xu J, Zhang T, Qi J, et al. A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis. Eur Radiol. 2022;32(4):2188–99.

PubMed  Google Scholar 

• Ma L, Wang Y, Guo L, Zhang Y, Wang P, Pei X, et al. Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning. J Xray Sci Technol. 2020;28(5):939–51. The project outlines the utilization of an AI tool to detect active pulmonary TB from CT images with an AUC value of 0.98.

PubMed  Google Scholar 

Cancer [Internet]. [cited 2023 Apr 4]. Available from: https://www.who.int/news-room/fact-sheets/detail/cancer

Gould MK, Donington J, Lynch WR, Mazzone PJ, Midthun DE, Naidich DP, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e93S-e120S.

PubMed  PubMed Central  Google Scholar 

Callister MEJ, Baldwin DR, Akram AR, Barnard S, Cane P, Draffan J, et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax. 2015;70(Suppl 2):ii1–54.

MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228–43.

PubMed  Google Scholar 

Loverdos K, Fotiadis A, Kontogianni C, Iliopoulou M, Gaga M. Lung nodules: a comprehensive review on current approach and management. Ann Thorac Med. 2019;14(4):226–38.

PubMed  PubMed Central  Google Scholar 

• Homayounieh F, Digumarthy S, Ebrahimian S, Rueckel J, Hoppe BF, Sabel BO, et al. An artificial intelligence-based chest X-ray model on human nodule detection accuracy from a multicenter study. JAMA Netw Open. 2021;4(12): e2141096. In this study, an AI algorithm was associated with improved detection of pulmonary nodules on CXR compared with unaided interpretation.

PubMed  PubMed Central  Google Scholar 

Zhang C, Sun X, Dang K, Li K, Guo X-W, Chang J, et al. Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncologist. 2019;24(9):1159–65.

PubMed  PubMed Central  Google Scholar 

Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, et al. Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol. 2016;30(6):71.

Google Scholar 

Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018;15(11): e1002711.

PubMed  PubMed Central  Google Scholar 

• Chen Y, Tian X, Fan K, Zheng Y, Tian N, Fan K. The value of artificial intelligence film reading system based on deep learning in the diagnosis of non-small-cell lung cancer and the significance of efficacy monitoring: a retrospective, clinical, nonrandomized, controlled study. Comput Math Methods Med. 2022;22(2022):2864170. The article states that deep learning has high sensitivity for detecting non-small cell lung cancer on CT images.

Google Scholar 

Chan J, Auffermann WF. Artificial intelligence in the imaging of diffuse lung disease. Radiol Clin North Am. 2022;60(6):1033–40.

PubMed  Google Scholar 

Raghu G, Remy-Jardin M, Myers JL, Richeldi L, Ryerson CJ, Lederer DJ, et al. Diagnosis of idiopathic pulmonary fibrosis. An official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2018;198(5):e44–8.

•• Bermejo-Peláez D, Ash SY, Washko GR, San José Estépar R, Ledesma-Carbayo MJ. Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks. Sci Rep. 2020;10(1):338. Deep convolutional neural networks can be applied to CT images to identify interstitial lung abnormalities with high diagnostic confidence.

Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. 2018;6(11):837–45.

PubMed  Google Scholar 

Yoo SJ, Yoon SH, Lee JH, Kim KH, Choi HI, Park SJ, et al. Automated lung segmentation on chest computed tomography images with ex