Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers

Cited by (1,001)

Items 301–400 of 1,001

1. 3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: Feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction
   Salvatore Gitto, Valentina Corino, Alessio Annovazzi, Estevāo Milazzo Machado, Marco Bologna, Lorenzo Marzorati, Domenico Albano, Carmelo Messina, Francesca Serpi, Vincenzo Anelli et al. · 2022 · Frontiers in Oncology
2. Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi‐task Model
   Kai Wang, Nicholas A. George‐Jones, Liyuan Chen, Jacob B. Hunter, Jing Wang · 2022 · The Laryngoscope
3. Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study
   Hossein Mohammad‐Rahimi, Shankeeth Vinayahalingam, Erfan Mahmoudinia, Parisa Soltani, Stefaan Jozef Bergé, Joachim Krois, Falk Schwendicke · 2023 · Diagnostics
4. Artificial intelligence for automatic classification of needle EMG signals: A scoping review
   S. de Jonge, Wouter V. Potters, Camiel Verhamme · 2024 · Clinical Neurophysiology
5. Lessons learned in transitioning to AI in the medical imaging of COVID-19
   Issam M. El Naqa, Qiyuan Hu, Weijie Chen, Hui Li, Jordan D. Fuhrman, Naveena Gorre, Maryellen Lissak Giger · 2021 · Journal of medical imaging
6. Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization
   Lorenzo Ugga, Gaia Spadarella, Lorenzo Pinto, Renato Cuocolo, Arturo Brunetti · 2022 · Cancers
7. Evaluating artificial intelligence algorithms for use in veterinary radiology
   Steve K. Joslyn, Kate Alexander · 2022 · Veterinary Radiology & Ultrasound
8. Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset
   Mónica Bonfanti‐Gris, Ángel Herrera, Sergio Paraíso-Medina, Raúl Alonso-Calvo, Francisco Martínez‐Rus, Guillermo Pradíes · 2024 · Journal of Dentistry
9. A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer
   Edoardo Mastella, Francesca Calderoni, Luigi Manco, Martina Ferioli, Serena Medoro, Alessandro Turra, Melchiore Giganti, Antonio Stefanelli · 2025 · Physics and Imaging in Radiation Oncology
10. AI Applications for Thoracic Imaging: Considerations for Best Practice
    Eui Jin Hwang, Jin Mo Goo, Chang Min Park · 2025 · Radiology
11. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
    David Dreizin, Pedro V. Staziaki, Garvit Devmohan Khatri, Nicholas M. Beckmann, Zhaoyong Feng, Yuanyuan Liang, Zachary DelProposto, Maximiliano Klug, James Stephen Spann, Nathan Sarkar et al. · 2023 · Emergency Radiology
12. Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis
    Peiru Liu, Ying Hao Sun, Xinzhuo Zhao, Ying Yan · 2023 · BioMedical Engineering OnLine
13. AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
    Spaanderman, Douwe J., Marzetti, Matthew, Wan, Xinyi, Scarsbrook, Andrew F., Robinson, Philip, Oei, Edwin H.G., Visser, Jacob J., Hemke, Robert, van Langevelde, Kirsten, Hanff, David F. et al. · 2025 · eBioMedicine
14. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms
    Belue, Mason J., Harmon, Stephanie A., Lay, Nathan S., Daryanani, Asha, Phelps, Tim E., Choyke, Peter L., Turkbey, Baris · 2023 · Journal of the American College of Radiology
15. Machine learning with multimodal data for COVID-19
    Weijie Chen, Rui Carlos Sá, Yuntong Bai, Sandy A. Napel, Olivier Gevaert, Diane S. Lauderdale, Maryellen Lissak Giger · 2023 · Heliyon
16. Diagnostic accuracy of an artificial intelligence algorithm versus radiologists for fracture detection on cervical spine CT
    Gaby J. van den Wittenboer, Brigitta YM van der Kolk, Ingrid M. Nijholt, Eline Langius-Wiffen, Rogier A. van Dijk, Boudewijn A.A.M. van Hasselt, Martin Podlogar, Wimar A. van den Brink, Gerrit J. Bouma, Niels W. L. Schep et al. · 2024 · European Radiology
17. AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study
    Mohammad Talal Elhakim, Sarah Wordenskjold Stougaard, Ole Graumann, Mads Nielsen, Oke Gerke, Lisbet Brønsro Larsen, Benjamin Schnack Rasmussen · 2024 · Radiology Artificial Intelligence
18. Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies
    Maurizio Cè, Marius Dumitru Chiriac, Andrea Cozzi, Laura Macrì, Francesca Lucrezia Rabaiotti, Giovanni Irmici, Deborah Fazzini, Gianpaolo Carrafiello, Michaela Iva Cellina · 2024 · Diagnostics
19. AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks
    Mohamed Slim, Reinhilde Jacobs, Renata Maíra de Souza Leal, Rocharles Cavalcante Fontenele · 2024 · Clinical Oral Investigations
20. Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models
    Paul Calle, Averi Bates, Justin C. Reynolds, Yunlong Liu, Haoyang Cui, Sinaro Ly, Chen Wang, Qinghao Zhang, Alberto J. de Armendi, Shashank S. Shettar et al. · 2025 · Computer Methods and Programs in Biomedicine
21. An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies
    Nina Pötsch, Matthias Dietzel, Panagiotis Kapetas, Paola Clauser, Katja Pinker, Stephan Ellmann, Michael Uder, Thomas H. Helbich, Pascal Baltzer · 2021 · European Radiology
22. Assessing Methods and Tools to Improve Reporting, Increase Transparency, and Reduce Failures in Machine Learning Applications in Health Care
    Christian Garbin, Oge Marques · 2022 · Radiology Artificial Intelligence
23. A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability
    Jingyu Zhong, Yangfan Hu, Yue Xing, Xiang Ge, Defang Ding, Huan Zhang, Weiwu Yao · 2022 · Insights into Imaging
24. Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance
    Mert Karabacak, Burak Berksu Ozkara, Admir Öztürk, Busra Kaya, Zeynep Cirak, Ece Orak, Zeynep Ozcan · 2022 · Acta Radiologica
25. THA-AID: Deep Learning Tool for Total Hip Arthroplasty Automatic Implant Detection With Uncertainty and Outlier Quantification
    Pouria Rouzrokh, John P. Mickley, Bardia Khosravi, Shahriar Faghani, Mana Moassefi, William R. Schulz, Bradley James Erickson, Michael J. Taunton, Cody C. Wyles · 2023 · The Journal of Arthroplasty
26. Translation of predictive modeling and AI into clinics: a question of trust
    Julian Caspers · 2021 · European Radiology
27. Diagnosis of COVID-19 Using CT image Radiomics Features: A Comprehensive Machine Learning Study Involving 26,307 Patients
    Isaac Shiri, Yazdan Salimi, Abdollah Saberi, Masoumeh Pakbin, Ghasem Hajianfar, Atlas Haddadi Avval, Amirhossein Sanaat, Azadeh Akhavanallaf, Shayan Mostafaei, Zahra Mansouri et al. · 2021 · medRxiv
28. Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies
    Jérémy Hofmeister, Nicolás Garin, Xavier Montet, Max Scheffler, Alexandra Platon, Pierre‐Alexandre Poletti, Jérôme Stirnemann, Marie‐Pierre Debray, Yann-Érick Claessens, Xavier Duval et al. · 2024 · European Radiology Experimental
29. DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR
    Marco Guglielmo, Marco Penso, Maria Ludovica Carerj, Carlo Maria Giacari, Alessandra Volpe, Laura Fusini, Andrea Baggiano, Saima Mushtaq, Andrea Daniele Annoni, Francesco Cannata et al. · 2024 · Atherosclerosis
30. Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario
    Aidan Boyd, Zezhong Ye, Sanjay P. Prabhu, Michael C. Tjong, Yining Zha, Anna Zapaishchykova, Sridhar Vajapeyam, Paul J. Catalano, Hasaan Hayat, Rishi Chopra et al. · 2024 · Radiology Artificial Intelligence
31. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility
    Evangelos K. Oikonomou, Rohan Khera · 2024 · Hellenic Journal of Cardiology
32. AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography
    Md Abu Sufian, Wahiba Hamzi, Tazkera Sharifi, Sadia Zaman, Lujain Alsadder, Esther Lee, Amir Hakim, Boumediene Hamzi · 2024 · Journal of Personalized Medicine
33. Pitfalls and Best Practices in Evaluation of AI Algorithmic Biases in Radiology
    Paul Hyunsoo Yi, Preetham Bachina, Beepul Bharti, Sean P. Garin, Adway Kanhere, Pranav Kulkarni, David K.B. Li, Vishwa S. Parekh, Samantha M. Santomartino, Linda P. Moy et al. · 2025 · Radiology
34. Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis
    Robert J. O’Shea, Amy Rose Sharkey, Gary Cook, Vicky Goh · 2021 · European Radiology
35. Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyond
    Moacir Antonelli Ponti, Fernando Pereira dos Santos, Leo S. F. Ribeiro, Gabriel B. Cavallari · 2021 · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
36. Diagnosis of Interproximal Caries Lesions in Bitewing Radiographs Using a Deep Convolutional Neural Network-Based Software
    Ángel García-Cañas, Mónica Bonfanti‐Gris, Sergio Paraíso-Medina, Francisco Martínez‐Rus, Guillermo Pradíes · 2022 · Caries Research
37. Diagnostic performance of magnetic resonance imaging–based machine learning in Alzheimer’s disease detection: a meta-analysis
    Jiayi Hu, Yashan Wang, Dingjie Guo, Zihan Qu, Chuanying Sui, Guangliang He, Song Wang, Xiaofei Chen, Chunpeng Wang, Xin Liu · 2022 · Neuroradiology
38. Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine
    Burak Koçak, Aytül Hande Yardımcı, Sabahattin Yüzkan, Ali Keles, Omer Altun, Elif Bulut, Osman Nuri Bayrak, Ahmet Arda Okumus · 2022 · Academic Radiology
39. Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning
    Zhenxing Huang, Wenbo Li, Yaping Wu, Guo Nannan, Lin Yang, Na Zhang, Zhi‐Feng Pang, Yongfeng Yang, Yun Zhou, Yue Shang et al. · 2023 · European Journal of Nuclear Medicine and Molecular Imaging
40. Deep learning and clustering approaches for dental implant size classification based on periapical radiographs
    Ji-Hyun Park, Hong‐Seok Moon, Hoi‐In Jung, Jae Joon Hwang, Yoon-Ho Choi, Jong‐Eun Kim · 2023 · Scientific Reports
41. External validation of an artificial intelligence solution for the detection of elbow fractures and joint effusions in children
    Michel Dupuis, Léo Delbos, Alexandra Rouquette, C. Adamsbaum, Raphaël Veil · 2023 · Diagnostic and Interventional Imaging
42. Fully automatic summarization of radiology reports using natural language processing with large language models
    Mizuho Nishio, Takaaki Matsunaga, Hidetoshi Matsuo, Munenobu Nogami, Yasuhisa Kurata, Koji Fujimoto, Osamu Sugiyama, Toshiaki Akashi, Shigeki Aoki, Takamichi Murakami · 2024 · Informatics in Medicine Unlocked
43. Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology
    Burak Koçak, Leonid L. Chepelev, Linda Chi Hang Chu, Renato Cuocolo, Brendan S. Kelly, Philipp Seeböck, Yee Liang Thian, Robbert Willem van Hamersvelt, Alan Wang, Stuart M. Williams et al. · 2023 · European Radiology
44. Deep learning in rheumatological image interpretation
    Stoel, Berend C., Staring, Marius, Reijnierse, Monique, van der Helm-van Mil, Annette H. M. · 2024 · Nature Reviews Rheumatology
45. Automated diagnosis of autism with artificial intelligence: State of the art
    Amir Valizadeh, Mana Moassefi, Amin Nakhostin-Ansari, Soheil Heidari Some’eh, Seyed Hossein Hosseini-Asl, Mehrnush Saghab Torbati, Reyhaneh Aghajani, Zahra Maleki Ghorbani, Iman Menbari Oskouie, Faezeh Aghajani et al. · 2023 · Reviews in the Neurosciences
46. Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis
    Yi Zhao, Andrew Coppola, Urvi Karamchandani, Dimitri Amiras, Chinmay Madhukar Gupte · 2024 · European Radiology
47. Deep learning performance compared to healthcare experts in detecting wrist fractures from radiographs: A systematic review and meta-analysis
    V. Hansen, Janni Jensen, Martin Weber Kusk, Oke Gerke, Hans B. Tromborg, Simon Lysdahlgaard · 2024 · European Journal of Radiology
48. Data set terminology of deep learning in medicine: a historical review and recommendation
    Shannon Leigh Walston, Hiroshi Seki, Hirotaka Takita, Yasuhito Mitsuyama, Shingo Sato, Akifumi Hagiwara, Rintaro Ito, Shouhei Hanaoka, Yukio Miki, Daiju Ueda · 2024 · Japanese Journal of Radiology
49. The Picasso’s skepticism on computer science and the dawn of generative AI: questions after the answers to keep “machines-in-the-loop”
    Filippo Pesapane, Renato Cuocolo, Francesco Sardanelli · 2024 · European Radiology Experimental
50. Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field
    Marta Bonada, Luca Francesco Rossi, Giovanni Carone, Flavio Panico, Fabio Cofano, Pietro Fiaschi, Diego Garbossa, Francesco Di Meco, Andrea Bianconi · 2024 · Biomedicines
51. Trustworthiness of Artificial Intelligence Models in Radiology and the Role of Explainability
    Felipe Kitamura, Oge Marques · 2021 · Journal of the American College of Radiology
52. Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma
    Aaron E. Kornblith, Newton Addo, Ruolei Dong, Robert Rogers, Jacqueline Grupp‐Phelan, Atul Janardhan Butte, Pavan Gupta, Rachael A. Callcut, Rima Arnaout · 2021 · Journal of Ultrasound in Medicine
53. Maskless 2-Dimensional Digital Subtraction Angiography Generation Model for Abdominal Vasculature using Deep Learning
    Hiroki Yonezawa, Daiju Ueda, Akira Yamamoto, Ken Kageyama, Shannon Leigh Walston, Takehito Nota, Kazuki Murai, Satoyuki Ogawa, Etsuji Sohgawa, Atsushi Jogo et al. · 2022 · Journal of Vascular and Interventional Radiology
54. Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset
    Massimiliano Bassi, A. Russomando, Jacopo Vannucci, Andrea Ciardiello, Miriam Dolciami, Paolo Ricci, Angelina Pernazza, Giulia d’Amati, Carlo Mancini Terracciano, R. Faccini et al. · 2022 · Translational Lung Cancer Research
55. Quality of reporting in AI cardiac MRI segmentation studies – A systematic review and recommendations for future studies
    Samer Alabed, Ahmed Maiter, Mahan Salehi, Aqeeb Mahmood, Sonali Daniel, Sam Jenkins, Marcus Goodlad, Michael J. Sharkey, Michail Mamalakis, Vera Rakocevic et al. · 2022 · Frontiers in Cardiovascular Medicine
56. Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning
    Jordan Chamberlin, Gilberto J. Aquino, Sophia Nance, Andrew Wortham, Nathan Leaphart, Namrata Paladugu, Sean Brady, Henry Baird, Matthew J. Fiegel, Logan Fitzpatrick et al. · 2022 · BMC Infectious Diseases
57. Hurdles to Artificial Intelligence Deployment: Noise in Schemas and “Gold” Labels
    Abdalla, Mohamed, Fine, Benjamin · 2023 · Radiology: Artificial Intelligence
58. Can a Deep Learning Algorithm Improve Detection of Occult Scaphoid Fractures in Plain Radiographs? A Clinical Validation Study
    Alfred P. Yoon, William T. Chung, Chien-Wei Wang, Chang‐Fu Kuo, Chi‐Hung Lin, Kevin C. Chung · 2023 · Clinical Orthopaedics and Related Research
59. Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence System
    Nancy Pham, Virginia B. Hill, Andreas M. Rauschecker, Yvonne W. Lui, Sumit N. Niogi, Christopher G. Filippi, Peter Chang, Greg Zaharchuk, Max Wintermark · 2023 · American Journal of Neuroradiology
60. Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA
    Adrian P. Brady, Bibb Allen, Jaron Chong, Elmar Kotter, Nina E. Kottler, John Mongan, Lauren Oakden‐Rayner, Daniel Pinto dos Santos, An Tang, Christoph Wald et al. · 2024 · Journal of the American College of Radiology
61. Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review
    Aykut Eken, Farhad Nassehi, Osman Eroğul · 2024 · Reviews in the Neurosciences
62. Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress
    Martin Schilling, Christina Unterberg‐Buchwald, Joachim Lotz, Martin Uecker · 2024 · Scientific Reports
63. Multicenter PET image harmonization using generative adversarial networks
    David Haberl, Clemens P. Spielvogel, Zewen Jiang, Fanny Orlhac, David Iommi, Ignasi Carrió, Irène Buvat, Alexander Robert Haug, László Papp · 2024 · European Journal of Nuclear Medicine and Molecular Imaging
64. Development and validation of a smartphone-based deep-learning-enabled system to detect middle-ear conditions in otoscopic images
    Constance Dubois, David Eigen, François Simon, Vincent Couloigner, Michael J. Gormish, Martin Chalumeau, Laurent Schmoll, Jérémie F. Cohen · 2024 · npj Digital Medicine
65. Houston, We Have AI Problem! Quality Issues with Neuroimaging‐Based Artificial Intelligence in Parkinson's Disease: A Systematic Review
    Verena Dzialas, Elena Doering, Helena Eich, Antonio P. Strafella, David E. Vaillancourt, Kristina Simonyan, Thilo van Eimeren, International Parkinson Movement Disorders Society‐Neuroimaging Study Group · 2024 · Movement Disorders
66. A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging
    Sébastien Molière, Dimitri Hamzaoui, Guillaume Ploussard, Romain Mathiéu, G. Fiard, Michaël Baboudjian, Benjamin Granger, Morgan Rouprêt A, Hervé Delingette, Raphaële Renard‐Penna · 2024 · European Urology Oncology
67. Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU‐Net
    Haihua Zhu, Haojie Yu, Fan Zhang, Zheng Cao, Fuli Wu, Fudong Zhu · 2022 · International Journal of Paediatric Dentistry
68. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
    Gabriel Cassinelli Petersen, J. Shatalov, T. Verma, W.R. Brim, H. Subramanian, A. Brackett, R.C. Bahar, S. Merkaj, T. Zeevi, L.H. Staib et al. · 2022 · American Journal of Neuroradiology
69. Machine learning imaging applications in the differentiation of true tumour progression from treatment‐related effects in brain tumours: A systematic review and meta‐analysis
    Abhishta Bhandari, Ravi Marwah, Justin Smith, Duy Anh Nguyễn, Asim Bhatti, Chee Peng Lim, Arian Lasocki · 2022 · Journal of Medical Imaging and Radiation Oncology
70. The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography
    Maryam Shahnavazi, Hosein Mohamadrahimi · 2023 · Dental Research Journal
71. Associating Peritoneal Metastasis With T2‐Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study
    Mingxiang Wei, Yu Zhang, Cong Ding, Jianye Jia, Haimin Xu, Yao Dai, Guannan Feng, Cai Qin, Genji Bai, Shuangqing Chen et al. · 2023 · Journal of Magnetic Resonance Imaging
72. Deep learning enables the differentiation between early and late stages of hip avascular necrosis
    Michail E. Klontzas, Evangelia E. Vassalou, Konstantinos Spanakis, Felix Meurer, Klaus Woertler, Aristeidis H. Zibis, Kostas Marias A, Apostolos H. Karantanas · 2023 · European Radiology
73. Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study
    Chih‐Hung Wang, Tzuching Lin, Guan‐Ru Chen, Meng‐Rui Lee, Joyce Tay, Ching‐Yi Wu, Meng‐Che Wu, Holger R. Roth, Dong Yang, Can Zhao et al. · 2023 · Journal of Medical Systems
74. Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging
    Wang, Ting-Wei, Shiao, Yu-Chieh, Hong, Jia-Sheng, Lee, Wei-Kai, Hsu, Ming-Sheng, Cheng, Hao-Min, Yang, Huai-Che, Lee, Cheng-Chia, Pan, Hung-Chuan, You, Weir Chiang et al. · 2024 · Mayo Clinic Proceedings: Digital Health
75. Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset
    Michail E. Klontzas, Georgios Kalarakis, Emmanouil Koltsakis, Thomas G. Papathomas, Apostolos H. Karantanas, Antonios Tzortzakakis · 2024 · Insights into Imaging
76. A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis
    Alexei Botnari, Manuella Kadar, Jenel Marian Pătraşcu · 2024 · Diagnostics
77. A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography
    Zeynab Pirayesh, Hossein Mohammad‐Rahimi, Saeed Reza Motamedian, Sepehr Amini Afshar, Reza Abbasi, Mohammad Hossein Rohban, Mina Mahdian, Mitra Ghazizadeh Ahsaie, Mina Iranparvar Alamdari · 2024 · BMC Oral Health
78. Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation
    Zahra Bashir, Manxi Lin, Aasa Feragen, Kamil Wojciech Mikolaj, Caroline Taksøe‐Vester, Anders Nymark Christensen, Morten Bo Søndergaard Svendsen, Mette Hvilshøj Fabricius, Lisbeth Anita Andreasen, Mads Nielsen et al. · 2025 · Scientific Reports
79. Towards precision MRI biomarkers in epilepsy with normative modelling
    Remika Mito, James Howard Cole, Sila Genc, Graeme D. Jackson, Andrew Zalesky · 2025 · Brain
80. Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective
    Jérémy Dana, Vincent Agnus, Farid Ouhmich, B. Gallix · 2020 · Seminars in Nuclear Medicine
81. Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk
    Noémie Johnston, Jeffrey De Rycke, Yolande N. Lievens, M. Van Eijkeren, Jan Aelterman, Eva Vandersmissen, Stephan Ponte, Barbara Vanderstraeten · 2022 · Physics and Imaging in Radiation Oncology
82. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative
    Andrea Ponsiglione, Arnaldo Stanzione, Gaia Spadarella, Agah Baran, Luca Alessandro Cappellini, Kevin B. W. Groot Lipman, Peter M. A. van Ooijen, Renato Cuocolo · 2022 · European Radiology
83. Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)
    Dong Keon Lee, Jin Hyuk Kim, Jaehoon Oh, Tae Hyun Kim, Myeong Seong Yoon, Dong Jin Im, Jae Ho Chung, Hayoung Byun · 2022 · Scientific Reports
84. Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features
    Jing Zhang, Chenao Zhan, Chenxiu Zhang, Yang Sup Song, Yan Lei Xu, Yihao Guo, Tao Ai, Guang Yang · 2023 · La radiologia medica
85. Deep learning based identification of bone scintigraphies containing metastatic bone disease foci
    Abdalla Ibrahim, Akshayaa Vaidyanathan, Sergey P. Primakov, Flore Belmans, Fabio Bottari, Turkey Refaee, Pierre Lovinfosse, Alexandre Jadoul, Céline Derwael, Fabian Hertel et al. · 2023 · Cancer Imaging
86. Artificial intelligence 101 for veterinary diagnostic imaging
    Adrien‐Maxence Hespel, Youshan Zhang, Parminder S. Basran · 2022 · Veterinary Radiology & Ultrasound
87. Benchmarking framework for machine learning classification from fNIRS data
    Johann Benerradi, Jérémie Clos, Aleksandra Landowska, Michel Valstar, Max L. Wilson · 2023 · Frontiers in Neuroergonomics
88. Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study
    Pelin Senem Özsunkar, Duygu Çelik Özen, Ahmed Z. Abdelkarim, Sacide Duman, Mehmet Uğurlu, Mehmet Rıdvan Demİr, Batuhan Kuleli, Özer Çelik, Büşra Seda İmamoğlu, İbrahim Şevki Bayrakdar et al. · 2024 · BMC Oral Health
89. Chest x-ray automated triage: A semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures
    Candelaria Mosquera, Facundo Nahuel Díaz, Fernando Binder, José M. Rabellino, Sonia Benítez, Alejandro Daniel Beresñak, Alberto Seehaus, Gabriel Ducrey, Jorge Ocantos, Daniel Roberto Luna · 2021 · Computer Methods and Programs in Biomedicine
90. Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence
    Annika Reinke, Minu D. Tizabi, Matthias Eisenmann, Lena Maier‐Hein · 2021 · European Urology Focus
91. Artificial intelligence for medical image analysis in epilepsy
    John Sollee, Lei Tang, Aime Bienfait Igiraneza, Bo Xiao, Harrison Xiao Bai, Yang Li · 2022 · Epilepsy Research
92. Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis
    Ryan C. Bahar, Sara Merkaj, Gabriel Cassinelli Petersen, Niklas Tillmanns, Harry E. Subramanian, Waverly Rose Brim, Tal Zeevi, Lawrence H. Staib, Eve Kazarian, MingDe Lin et al. · 2022 · Frontiers in Oncology
93. DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules
    Chengdi Wang, Jun Shao, Xiuyuan Xu, Le Yi, Pei Liu, Yale Yang, Jixiang Guo, Yanqi He, Lei Zhang, Yi Zhang et al. · 2021 · Frontiers in Oncology
94. Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review
    Paul Y. Windisch, Carole Koechli, Susanne J. Rogers, Christina Schröder, Robert E Forster, Daniel R. Zwahlen, Stephan B. Bodis · 2022 · Cancers
95. AI Based Diagnosis of Pneumonia
    B. Vidhya, M. Nikhil Madhav, M. Suresh Kumar, S. Kalanandini · 2022 · Wireless Personal Communications
96. Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach
    Mert Karabacak, Burak Berksu Ozkara, Seren Mordag, Sotirios Bisdas · 2022 · Quantitative Imaging in Medicine and Surgery
97. Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis
    Jai Prashar, Nicole Tay · 2023 · Eye
98. Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non-comprehensive stroke center
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99. Self-reported checklists and quality scoring tools in radiomics: a meta-research
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