{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T11:21:33Z","timestamp":1787916093728,"version":"build-2784847793"},"reference-count":66,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and integration into diagnostic pathways. We conducted a narrative, state-of-the-art review of AI applications across the TB diagnosis pathway. Evidence was synthesized from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies published between 2010 and 2026, with a structured selection process aligned with PRISMA principles. CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged \u226515 years in specific contexts. Across studies, CAD-CXR demonstrates sensitivity comparable to human readers, although performance varies by product, population, and imaging conditions, necessitating local threshold calibration. Evidence from implementation studies suggests improvements in screening efficiency and potential cost-effectiveness in high-burden settings. Other AI modalities, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction show promising but heterogeneous results, with most requiring further independent validation and prospective evaluation. AI has the potential to strengthen TB screening and diagnostic pathways, but its impact depends on integration into health systems and evaluated using patient- and program-level outcomes rather than accuracy alone. A differentiated approach is needed, with responsible scale-up of policy-endorsed tools alongside rigorous evaluation of emerging technologies to support effective and equitable TB care.<\/jats:p>","DOI":"10.3390\/diagnostics16081127","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:21:32Z","timestamp":1775737292000},"page":"1127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation"],"prefix":"10.3390","volume":"16","author":[{"given":"Hien Thi Thu","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Molecular Diagnostics, Aalborg University Hospital, 9260 Aalborg, Denmark"},{"name":"Department of Clinical Medicine, Aalborg University, 9260 Aalborg, Denmark"},{"name":"AVSE Global Medical Translational Research Network, 75008 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vang","family":"Le-Quy","sequence":"additional","affiliation":[{"name":"Novodan ApS, 9220 Aalborg, Denmark"},{"name":"AVSE Global Data Science Network, 75008 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8651-5176","authenticated-orcid":false,"given":"Anh Tuan","family":"Dinh-Xuan","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine and Physiology, Cochin University Hospital, 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4820-6967","authenticated-orcid":false,"given":"Linh Nhat","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department for HIV, Tuberculosis, Hepatitis and Sexually Transmitted Infections, World Health Organization (WHO) Headquarters in Geneva, 1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Storla, D.G., Yimer, S., and Bjune, G.A. (2008). A Systematic Review of Delay in the Diagnosis and Treatment of Tuberculosis. BMC Public Health, 8.","DOI":"10.1186\/1471-2458-8-15"},{"key":"ref_2","unstructured":"WHO, and Global Programme on Tuberculosis and Lung Health (GTB) (2025). Global Tuberculosis Report 2025, WHO."},{"key":"ref_3","unstructured":"WHO, and Global Programme on Tuberculosis and Lung Health (GTB) (2021). WHO Consolidated Guidelines on Tuberculosis: Module 2: Screening: Systematic Screening for Tuberculosis Disease, WHO."},{"key":"ref_4","unstructured":"WHO (2025). Use of Computer-Aided Detection Software for Tuberculosis Screening: WHO Policy Statement, WHO."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15000","DOI":"10.1038\/s41598-019-51503-3","article-title":"Using Artificial Intelligence to Read Chest Radiographs for Tuberculosis Detection: A Multi-Site Evaluation of the Diagnostic Accuracy of Three Deep Learning Systems","volume":"9","author":"Qin","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1093\/cid\/ciab644","article-title":"Digital Chest Radiography Enhances Screening Efficiency for Pulmonary Tuberculosis in Primary Health Clinics in South Africa","volume":"74","author":"Moodley","year":"2022","journal-title":"Clin. Infect. Dis."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Subbaraman, R., Nathavitharana, R.R., Mayer, K.H., Satyanarayana, S., Chadha, V.K., Arinaminpathy, N., and Pai, M. (2019). Constructing Care Cascades for Active Tuberculosis: A Strategy for Program Monitoring and Identifying Gaps in Quality of Care. PLoS Med., 16.","DOI":"10.1371\/journal.pmed.1002754"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1093\/cid\/ciaf149","article-title":"Machine Learning-Based Prediction of Active Tuberculosis in People with HIV Using Clinical Data","volume":"81","author":"Bartl","year":"2025","journal-title":"Clin. Infect. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bigio, J., Kohli, M., Klinton, J.S., MacLean, E., Gore, G., Small, P.M., Ruhwald, M., Weber, S.F., Jha, S., and Pai, M. (2021). Diagnostic Accuracy of Point-of-Care Ultrasound for Pulmonary Tuberculosis: A Systematic Review. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251236"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10063","DOI":"10.1038\/ncomms10063","article-title":"Rapid Antibiotic-Resistance Predictions from Genome Sequence Data for Staphylococcus aureus and Mycobacterium tuberculosis","volume":"6","author":"Bradley","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_11","first-page":"939","article-title":"Developing and Verifying Automatic Detection of Active Pulmonary Tuberculosis from Multi-Slice Spiral CT Images Based on Deep Learning","volume":"28","author":"Ma","year":"2020","journal-title":"J. X-Ray Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s44163-024-00179-4","article-title":"Detection of Tuberculosis Using Cough Audio Analysis: A Deep Learning Approach with Capsule Networks","volume":"4","author":"Rajasekar","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_13","unstructured":"FIND (2026, February 07). Validation Platform for AI-Based Diagnostic Evaluation. Available online: https:\/\/www.finddx.org\/what-we-do\/projects\/validation-platform-for-ai-based-diagnostic-evaluation\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1148\/radiol.2017171920","article-title":"Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction","volume":"286","author":"Park","year":"2018","journal-title":"Radiology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do No Harm: A Roadmap for Responsible Machine Learning for Health Care","volume":"25","author":"Wiens","year":"2019","journal-title":"Nat. Med."},{"key":"ref_18","unstructured":"WHO (2022). WHO Operational Handbook on Tuberculosis, WHO."},{"key":"ref_19","first-page":"392","article-title":"The Natural History of Childhood Intra-Thoracic Tuberculosis: A Critical Review of Literature from the Pre-Chemotherapy Era","volume":"8","author":"Marais","year":"2004","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1164\/ajrccm.159.4.9806083","article-title":"Pulmonary Auscultatory Skills during Training in Internal Medicine and Family Practice","volume":"159","author":"Mangione","year":"1999","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Garg, T., John, S., Abdulkarim, S., Ahmed, A.D., Kirubi, B., Rahman, M.T., Ubochioma, E., and Creswell, J. (2025). Implementation Costs and Cost-Effectiveness of Ultraportable Chest X-Ray with Artificial Intelligence in Active Case Finding for Tuberculosis in Nigeria. PLoS Digit. Health, 4.","DOI":"10.1371\/journal.pdig.0000894"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1513\/AnnalsATS.202103-380OC","article-title":"Digital Chest X-Ray with Computer-Aided Detection for Tuberculosis Screening within Correctional Facilities","volume":"19","author":"Velen","year":"2022","journal-title":"Ann. Am. Thorac. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e093989","DOI":"10.1136\/bmjopen-2024-093989","article-title":"Effectiveness and Cost-Effectiveness of Community-Based TB Screening Algorithms Using Computer-Aided Detection (CAD) Technology Alone Compared with CAD Combined with Point-of-Care C Reactive Protein Testing in Lesotho and South Africa: Protocol for a Pair","volume":"15","author":"Signorell","year":"2025","journal-title":"BMJ Open"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"E894","DOI":"10.1093\/cid\/ciac679","article-title":"The Performance of Computer-Aided Detection Digital Chest X-Ray Reading Technologies for Triage of Active Tuberculosis Among Persons with a History of Previous Tuberculosis","volume":"76","author":"Kagujje","year":"2023","journal-title":"Clin. Infect. Dis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Raval, D., Parmar, D., Saha, S., Sarkar, R., Wadhwa, M., Pandya, A., Shah, H., and Rajsekar, K. (2025). Cost-Effectiveness Analysis of AI-Assisted Chest X-Ray Interpretation Tools for TB Screening: A Rapid HTA. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1629127"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"ofaf572","DOI":"10.1093\/ofid\/ofaf572","article-title":"Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results from the CODA TB DREAM Challenge","volume":"12","author":"Jaganath","year":"2025","journal-title":"Open Forum Infect. Dis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.glt.2025.02.005","article-title":"Development and Evaluation of an Artificial Intelligence (AI) -Assisted Chest x-Ray Diagnostic System for Detecting, Diagnosing, and Monitoring Tuberculosis","volume":"7","author":"Kaewwilai","year":"2025","journal-title":"Glob. Transit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, Z., Liang, P., Liu, Z., Cai, H., and Sun, Q. (2024). TB-DROP: Deep Learning-Based Drug Resistance Prediction of Mycobacterium tuberculosis Utilizing Whole Genome Mutations. BMC Genom., 25.","DOI":"10.1186\/s12864-024-10066-y"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27091","DOI":"10.1038\/s41598-024-77947-w","article-title":"Leveraging Large-Scale Mycobacterium tuberculosis Whole Genome Sequence Data to Characterise Drug-Resistant Mutations Using Machine Learning and Statistical Approaches","volume":"14","author":"Pruthi","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_30","unstructured":"WHO, and Chief Scientist and Science Division (SCI) (2025). Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models, WHO."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bashir, S., Kik, S.V., Ruhwald, M., Khan, A., Tariq, M., Hussain, H., and Denkinger, C.M. (2022). Economic Analysis of Different Throughput Scenarios and Implementation Strategies of Computer-Aided Detection Software as a Screening and Triage Test for Pulmonary TB. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0277393"},{"key":"ref_33","unstructured":"IMDRF SaMD Working Group (2013). Software as a Medical Device (SaMD): Key Definitions, International Medical Device Regulators Forum."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1007\/s00330-021-08365-z","article-title":"A Fully Automatic Artificial Intelligence\u2013Based CT Image Analysis System for Accurate Detection, Diagnosis, and Quantitative Severity Evaluation of Pulmonary Tuberculosis","volume":"32","author":"Yan","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"27318","DOI":"10.1109\/ACCESS.2020.2971257","article-title":"Modality-Specific Deep Learning Model Ensembles Toward Improving TB Detection in Chest Radiographs","volume":"8","author":"Rajaraman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., and Oermann, E.K. (2018). Variable Generalization Performance of a Deep Learning Model to Detect Pneumonia in Chest Radiographs: A Cross-Sectional Study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002683"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"110706","DOI":"10.1016\/j.clinimag.2025.110706","article-title":"A Systematic Review and Meta-Analysis on the Diagnostic Performance of Chest Ultrasound for Pulmonary Tuberculosis","volume":"130","author":"Hassankhani","year":"2026","journal-title":"Clin. Imaging"},{"key":"ref_39","first-page":"275","article-title":"Point-of-care ultrasonography","volume":"101","author":"Arnold","year":"2020","journal-title":"Am. Fam. Physisian"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., and Mascolo, C. (2020). Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. KDD\u201920: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mini, Association for Computing Machinery.","DOI":"10.1145\/3394486.3412865"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1109\/OJEMB.2020.3026928","article-title":"COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings","volume":"1","author":"Laguarta","year":"2020","journal-title":"IEEE Open J. Eng. Med. Biol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"045005","DOI":"10.1088\/1361-6579\/aab6d0","article-title":"Detection of Tuberculosis by Automatic Cough Sound Analysis","volume":"39","author":"Botha","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pramono, R.X.A., Bowyer, S., and Rodriguez-Villegas, E. (2017). Automatic Adventitious Respiratory Sound Analysis: A Systematic Review. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177926"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"19","DOI":"10.12701\/jyms.2025.42.19","article-title":"Digital Auscultation in Clear and Present Threat of Novel Respiratory Infectious Disease: A Narrative Review","volume":"42","author":"Lee","year":"2025","journal-title":"J. Yeungnam Med. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/S1473-3099(10)70078-5","article-title":"Antiretrovirals and Isoniazid Preventive Therapy in the Prevention of HIV-Associated Tuberculosis in Settings with Limited Health-Care Resources","volume":"10","author":"Lawn","year":"2010","journal-title":"Lancet Infect. Dis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1701354","DOI":"10.1183\/13993003.01354-2017","article-title":"A Standardised Method for Interpreting the Association between Mutations and Phenotypic Drug Resistance in Mycobacterium tuberculosis","volume":"50","author":"Miotto","year":"2017","journal-title":"Eur. Respir. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1093\/bioinformatics\/btx801","article-title":"Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data","volume":"34","author":"Yang","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5487","DOI":"10.1038\/s41598-020-62368-2","article-title":"Artificial Intelligence and Machine Learning Based Prediction of Resistant and Susceptible Mutations in Mycobacterium tuberculosis","volume":"10","author":"Jamal","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_49","unstructured":"World Health Organisation (2023). Catalogue of Mutations in Mycobacterium tuberculosis Complex and Their Association with Drug Resistance, WHO. [2nd ed.]."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning Deep Features for Discriminative Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1038\/s41598-023-28079-0","article-title":"Deep Learning Classification of Active Tuberculosis Lung Zones Wise Manifestations Using Chest X-Rays: A Multi Label Approach","volume":"13","author":"Devasia","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.imed.2023.06.001","article-title":"Deep Learning Models for Tuberculosis Detection and Infected Region Visualization in Chest X-Ray Images","volume":"4","author":"Sharma","year":"2024","journal-title":"Intell. Med."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Vanitha, K., Mahesh, T.R., Kumar, V.V., and Guluwadi, S. (2025). Enhanced Tuberculosis Detection Using Vision Transformers and Explainable AI with a Grad-CAM Approach on Chest X-Rays. BMC Med. Imaging, 25.","DOI":"10.1186\/s12880-025-01630-3"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). \u201cWhy Should I Trust You?\u201d Explaining the Predictions of Any Classifier. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, Association for Computational Linguistics.","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref_56","first-page":"4766","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_57","first-page":"234","article-title":"INet: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Proceedings of the the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)"},{"key":"ref_58","unstructured":"Hwang, S., Kim, H.-E., Jeong, J., and Kim, H.-J. (March, January 27). A Novel Approach for Tuberculosis Screening Based on Deep Convolutional Neural Networks. Proceedings of the SPIE Medical Imaging, San Diego, CA, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ou, C.Y., Chen, I.Y., Chang, H.T., Wei, C.Y., Li, D.Y., Chen, Y.K., and Chang, C.Y. (2024). Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-Ray Images. Diagnostics, 14.","DOI":"10.3390\/diagnostics14090952"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A Survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_61","unstructured":"Perez, L., and Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","article-title":"GAN-Based Synthetic Medical Image Augmentation for Increased CNN Performance in Liver Lesion Classification","volume":"321","author":"Diamant","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and Flexible Image Augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_64","first-page":"2577","article-title":"On the Automatic Generation of Medical Imaging Reports","volume":"Volume 1","author":"Jing","year":"2018","journal-title":"ACL 2018\u201456th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Chen, Z., Song, Y., Chang, T.H., and Wan, X. (2020). Generating Radiology Reports via Memory-Driven Transformer. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.emnlp-main.112"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Aksoy, N., Ravikumar, N., and Frangi, A.F. (2023, January 19\u201324). Radiology Report Generation Using Transformers Conditioned with Non-Imaging Data. Proceedings of the Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, San Diego, CA, USA.","DOI":"10.1117\/12.2653672"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/8\/1127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:36:06Z","timestamp":1775738166000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/8\/1127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,9]]},"references-count":66,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["diagnostics16081127"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16081127","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,9]]}}}