Understanding artificial intelligence based radiology studies: What is overfitting?

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Untitled work

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Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898

Model Selection and Multimodel Inference

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Convolutional neural networks: an overview and application in radiology

Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Artificial intelligence in radiology

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719

Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging

Muller, J., Wang, R., Milddleton, D., Alizadeh, M., Kang, K. C., Hryczyk, R., Zabrecky, G. P., Hriso, C., Navarreto, E., Wintering, N., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1182509 · Cited by 11

Automated detection of crystalline retinopathy via fundus photography using multistage generative adversarial networks

Choi, E. Y., Han, S. H., Ryu, I. H., Kim, J. K., Lee, I. S., Han, E., Kim, H.-S., Choi, J. Y., & Yoo, T. K. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.10.005 · Cited by 11

Development of artificial intelligence-based algorithms for the process of human identification through dental evidence

Pereira, C. P., Carvalho, R., Augusto, D., Almeida, T., Francisco, A. P., Salvado, F., & Santos, R. (2025) · International Journal of Legal Medicine · doi:10.1007/s00414-025-03453-x · Cited by 11

Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning

Longo, U. G., Salvatore, S. D., Piccolomini, A., Ullman, N. S., Salvatore, G., D’Hooghe, M., Saccomanno, M. F., Samuelsson, K., Papalia, R., & Pareek, A. (2025) · Journal of Experimental Orthopaedics · doi:10.1002/jeo2.70195 · Cited by 11

Low-rank sparse feature selection with incomplete labels for Alzheimer's disease progression prediction

Chen, Z., Liu, Y., Zhang, Y., Jin, R., Tao, J., & Chen, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105705 · Cited by 11

A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays

Vardhan, A., Makhnevich, A., Omprakash, P., Hirschorn, D. S., Barish, M. A., Cohen, S. L., & Zanos, T. P. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-022-00103-0 · Cited by 12

Artificial Intelligence Applications in Musculoskeletal Imaging

Shariatnia, M., Bagherieh, S., Semnani, F., Rafiei, N., Avval, A. H., Ollivier, M. P., Musahl, V., & Pareek, A. (2025) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-025-09997-0 · Cited by 13

Prediction of midpalatal suture maturation stage based on transfer learning and enhanced vision transformer

Tang, H., Liu, S., Tan, W., Fu, L., Yan, M. G., & Feng, H. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02598-w · Cited by 10

A data augmentation approach based on various GAN models to address class imbalance in fine-grained multimodal fake news datasets

Hamed, S. K., Aziz, M. J. A., & Yaakub, M. R. (2025) · Computing · doi:10.1007/s00607-025-01413-2 · Cited by 10

Performance of Artificial Intelligence in Skin Cancer Detection: An Umbrella Review of Systematic Reviews and Meta‐Analyses

Karimzadhagh, S., Ghodous, S., ROBATI, R. M., Abbaspour, E., Goldust, M., Zaresharifi, N., & Zaresharifi, S. (2025) · International Journal of Dermatology · doi:10.1111/ijd.17981 · Cited by 15

Are Artificial Intelligence Models Reliable for Clinical Application in Pediatric Fracture Detection on Radiographs? A Systematic Review and Meta-analysis

Ximenes, G. F., Costa, Á. L., Leite, L. L., Costa, L. L., Ribeiro, M. O., Colares, P. G. B., & Cerqueira, G. S. (2025) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003660 · Cited by 11

The use of artificial intelligence in radiological diagnosis and detection of dental caries: a systematic review

Futyma-Gąbka, K., & Różyło‐Kalinowska, I. (2021) · Journal of Stomatology · doi:10.5114/jos.2021.111664 · Cited by 10

Scaling up cell-counting efforts in neuroscience through semi-automated methods

Bjerke, I. E., Yates, S. C., Carey, H., Bjaalie, J. G., & Leergaard, T. B. (2023) · iScience · doi:10.1016/j.isci.2023.107562 · Cited by 9

Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM)

Fang, Z., Johnston, A., Cheuy, L. Y., Na, H. S., Paschali, M., González, C., Armstrong, B. A., Koirala, A., Laurel, D., Campion, A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02007-0 · Cited by 11

Novel Method for Three-Dimensional Facial Expression Recognition Using Self-Normalizing Neural Networks and Mobile Devices

Hartmann, T. J., Hartmann, J., Friebe-Hoffmann, U., Lato, C., Janni, W. J., & Látó, K. (2022) · Geburtshilfe und Frauenheilkunde · doi:10.1055/a-1866-2943 · Cited by 9

Establishment of a feed intake prediction model based on eating time, ruminating time and dietary composition

Li, S., Chengrui, Z., Wang, R., Jiamei, S., Hangshu, X., Yonggen, Z., & Sun, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107296 · Cited by 8

Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy

Shin, T. Y., Han, H. H., Min, H., Cho, H., Kim, S., Park, S. Y., Kim, H. J., Kim, J. H., & Lee, Y. (2023) · Medicina · doi:10.3390/medicina59081402 · Cited by 9

Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy

Kaestner, E. J., Hassanzadeh, R., Gleichgerrcht, E. L., Hasenstab, K. A., Roth, R. W., Chang, A. J., Rüber, T., Davis, K. A., Dugan, P. C., Kuzniecky, R., et al. (2024) · Brain Communications · doi:10.1093/braincomms/fcae346 · Cited by 8

AI ‐Assisted Assessment of Inquiry Skills in Socioscientific Issue Contexts

Zhang, W.-X., Lin, J. J. H., & Hsu, Y. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13102 · Cited by 9

Quantitative CT imaging: where are we, and what is missing?

Treb, K. J., Sadaney, A. O. E., Ferrero, A., Baffour, F. I., Leng, S., Yu, L., Rajiah, P. S., Fletcher, J. G., McCollough, C. H., & Rajendran, K. (2025) · British Journal of Radiology · doi:10.1093/bjr/tqaf075 · Cited by 8

Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders

Engin, C. D., Gokkan, M. O., Köksaldı, S., Kayabaşı, M., Beşenk, U., Selver, M. A., & Grzybowski, A. E. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14082774 · Cited by 8

Novel CNN-Based Approach for Reading Urban Form Data in 2D Images: An Application for Predicting Restaurant Location in Seoul, Korea

Yang, J., & Kwon, Y.-S. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12090373 · Cited by 8

Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI Using Noisy Student-Based Training

Dikici, E., Nguyen, X. V., Bigelow, M. T., Ryu, J., & Prevedello, L. M. S. (2022) · Diagnostics · doi:10.3390/diagnostics12082023 · Cited by 8

Epigenetic Signatures in Hypertension

Alfonso, G., & Martinez, V. D. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13050787 · Cited by 10

Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence

Park, S. H., Choi, J., & Byeon, J. (2020) · Journal of Korean Medical Association · doi:10.5124/jkma.2020.63.11.696 · Cited by 8

Artificial Intelligence

Bottino, L., Settino, M., & Cannataro, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70310-2_2 · Cited by 9

Feasibility study of real-time virtual sensing for water quality parameters in river systems using synthetic data and deep learning models

Choi, B., Han, E. J., Lee, K., Son, M., Hong, S. W., Lee, S., & Chae, S. H. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.125191 · Cited by 7

Improving AI performance in wildlife monitoring through species and environment-specific training: A case study on desert Bighorn sheep

Okuley, O. S., Aiello, C. M., Glad, W., Perkins, K., Ianniello, R., Darby, N. W., & Epps, C. W. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103179 · Cited by 7

Developing surrogate models for holistic building retrofit optimization with multi-criteria decision-making support

Saad, M. M., & Eicker, U. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116358 · Cited by 7

Automated Captioning for Ergonomic Problem and Solution Identification in Construction Using a Vision-Language Model and Caption Augmentation

Yong, G., Liu, M., & Lee, S. H. (2024) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784485293.071 · Cited by 10

Predicting the survival of patients with glioblastoma using deep learning: a systematic review

Habibi, M. A., Tajabadi, Z., Farsani, A. S., Omid, R., Tajabadi, Z., & Shobeiri, P. (2025) · The Egyptian Journal of Neurosurgery : the official publication of the Egyptian Society of Neurological Surgeons/Egyptian journal of neurosurgery · doi:10.1186/s41984-025-00385-x · Cited by 8

Artificial Intelligence Models and Techniques Applied to COVID-19: A Review

Múñoz, L., Villarreal, V., Nielsen, M., González, Y. A. C., Sittón-Candanedo, I., & Corchado, J. M. (2021) · Electronics · doi:10.3390/electronics10232901 · Cited by 8

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study

Chang, J. T., Lin, B.-R., Wang, T., & Chen, C. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01260-1 · Cited by 8

Human-AI Complementarity in Diagnostic Radiology: The Case of Double Reading

Wagner, I. A., & Chakradeo, K. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00886-5 · Cited by 10

Quality over quantity? The role of data quality and uncertainty for AI in surgery

Jogan, M., Kurada, S., Vasisht, S., Singh, V., & Hashimoto, D. A. (2024) · Global Surgical Education - Journal of the Association for Surgical Education · doi:10.1007/s44186-024-00276-z · Cited by 9

Deep-learning-based data loss reconstruction for spatiotemporal temperature in piloti structures: Enhancing applicability with limited datasets

Kim, S.-J., Park, S., Shin, J., Choi, I.-R., & Choi, S.-M. (2023) · Fire Safety Journal · doi:10.1016/j.firesaf.2023.103887 · Cited by 6

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods

Johnson, L. A., Harmon, S. A., Yılmaz, E. C., Lin, Y., Belue, M. J., Merriman, K. M., Lay, N. S., Sanford, T., Sarma, K. V., Arnold, C. W., et al. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04242-7 · Cited by 6

Computer Vision-Based Multi-Feature Extraction and Regression for Precise Egg Weight Measurement in Laying Hen Farms

Jiang, Y., Atwa, E. M., He, P., Zhang, J., Di, M., Pan, J., & Lin, H. (2025) · Agriculture · doi:10.3390/agriculture15192035 · Cited by 7

Identification of Systems Having Unstable Dynamics and Time Delays Using Delayed Recurrent Neural Networks

Sharma, S., Prasad, S. V. S., & Arulananth, T. S. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08356-w · Cited by 5

Artificial intelligence and healthcare

Mittal, A., Afsar, A. P., Tayal, A., & Shetty, M. K. (2023) · MAMC Journal of Medical Sciences · doi:10.4103/mamcjms.mamcjms_27_23 · Cited by 5

Summative Assessment of Undergraduate Learning Outcomes with Cross-Validation in C Programming Course

Zhang, Y., Liang, R., Zheng, Y., Zhang, H., Wang, P., & Li, Y. (2022) · International Conference on Information and Education Technology (ICIET) · doi:10.1109/iciet55102.2022.9779036 · Cited by 5

Modeling and optimization study on degradation of organic contaminants using nZVI activated persulfate based on response surface methodology and artificial neural network: a case study of benzene as the model pollutant

Luo, M., Zhang, X., Long, T., Chen, S., Zhan, M., Zhu, X. H., & Yu, R. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1270730 · Cited by 5

Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study

Kengkard, P., Choovuthayakorn, J., Mahakkanukrauh, C., Chitapanarux, N., Intasuwan, P., Malatong, Y., Sinthubua, A., Palee, P., Nalampang, S., & Mahakkanukrauh, P. (2023) · Anatomy & Cell Biology · doi:10.5115/acb.22.205 · Cited by 5

Spatial Wave Measurement Based on U-net Convolutional Neural Network in Large Wave Flume

Chen, J., Hu, Y., Chen, S., Ren, Z., & ARIKAWA, T. (2023) · Water · doi:10.3390/w15040647 · Cited by 5

Applications of Artificial Intelligence in Life Sciences

Hoh, Y. K. (2025) · The American Biology Teacher · doi:10.1525/abt.2025.87.3.165 · Cited by 4

Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound

Viceconti, N., Andaloro, S., Paratore, M., Miliani, S., Giulia, D., Cerniglia, G., Mancuso, F., Melita, E., Gasbarrini, A., Riccardi, L., et al. (2026) · World Journal of Gastroenterology · doi:10.3748/wjg.v32.i2.113059 · Cited by 4

Imbalanced Samples Fault Detection Using GANCNN for Power Inverters

Sun, Q. (2022) · CPSS Transactions on Power Electronics and Applications · doi:10.24295/cpsstpea.2022.00040 · Cited by 4

COVision: convolutional neural network for the differentiation of COVID−19 from common pulmonary conditions using CT scans

Parikh, K. V., & Mathew, T. J. (2023) · BMC Pulmonary Medicine · doi:10.1186/s12890-023-02723-x · Cited by 4

Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis

Li, J., Chen, S.-N., Lin, Y.-Y., Wu, Y., Lu, W., Ye, D.-L., Chen, F., & Qiu, S. (2023) · The Turkish Journal of Gastroenterology · doi:10.5152/tjg.2023.22257 · Cited by 4

Applications of Artificial Intelligence in Mammography from a Development and Validation Perspective

Kim, K. H., & Lee, S. H. (2021) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2020.0205 · Cited by 5

The Future of Artificial Intelligence in Southeast Asia

Mongkol, K. (2022) · Advances in finance, accounting, and economics book series · doi:10.4018/978-1-6684-5849-5.ch002 · Cited by 4

Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages

Сморчкова, А. К., Khoruzhaya, A. N., Kremneva, E. I., & Petryaikin, A. V. (2023) · Burdenko s Journal of Neurosurgery · doi:10.17116/neiro20238702185 · Cited by 4

Classification of Floor Materials Using Piezoelectric Actuator–Sensor Pair and Deep Learning for Mobile Robots

Min, J., Pack, J., Park, H. I., & Cha, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3367435 · Cited by 4

Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?

Mutasa, S., & Yi, P. H. (2021) · Skeletal Radiology · doi:10.1007/s00256-021-03850-4 · Cited by 4

A numerical study on prediction of particle size distribution in CFB boiler using machine learning

Kwon, H. M., Yang, C. W., Park, G., Kim, S. I., Bang, B. R., Go, E. S., Kim, S. M., & Lee, U. (2025) · Powder Technology · doi:10.1016/j.powtec.2025.121989 · Cited by 3

Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records

Chi, C.-Y., Moghadas-Dastjerdi, H., Winkler, A., Ao, S., Chen, Y.-P., Wang, L.-W., Su, P., Lin, W., Tsai, M., & Huang, C. (2023) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2409265 · Cited by 4

Essentials for AI Research in Cardiology: Challenges and Mitigations

Jaltotage, B., & Dwivedi, G. (2024) · CJC Open · doi:10.1016/j.cjco.2024.07.015 · Cited by 5

Frequency of Maternal Morbidities in Patients with Placenta Previa - A Prospective Single-Centered Study in Hazara Division.

Khan, M., Khan, N., Noor, S., Hussain, A., Rahman, H. U., Irshad, S., & Khan, F. A. (2024) · Journal of Gandhara Medical and Dental Science · doi:10.37762/jgmds.11-4.616 · Cited by 3

Automatic Generation of Training Data for AI Object Detection in Terms of Technical Drawings in Engineering

Dillenhöfer, F., Doksanbir, A., Kraske, J. L., & Künne, B. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-42467-0_60 · Cited by 2

The Economics of Artificial Intelligence: Strategic Knowledge Arbitrage and Serendipity as Real Options Drivers in the AI Era

Carayannis, E. G., Vincenzi, M., & Erokhin, V. L. (2025) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-032-03127-3_7 · Cited by 3

MRI management of focal liver lesions: what a beginner cannot fail to know

Granata, V., Fusco, R., Simonetti, I., Riga, M. G., Pellegrino, G., Carriero, S., Karaboue, M. A. A., Carrafiello, G., Petrillo, A., & Izzo, F. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1630424 · Cited by 2

Recent Advances in Soil Stabilization and Reinforcement: A Comprehensive Review of Emerging Technologies

Hung, C., Chen, C.-S., Liu, C.-H., Lin, C.-Y., Hsu, C.-Y., Wang, Y.-W., & Lin, K.-Y. A. (2026) · Advances in Civil Engineering · doi:10.1155/adce/2303208 · Cited by 3

Digital transformation in healthcare

Masoud, R. (2022) · International Journal of Scientific and Research Publications · doi:10.29322/ijsrp.12.07.2022.p12744 · Cited by 4

Prediction of model generalizability for unseen data: Methodology and case study in brain metastases detection in T1-Weighted contrast-enhanced 3D MRI

Dikici, E., Nguyen, X. V., Takacs, N., & Prevedello, L. M. S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106901 · Cited by 2

Design and optimization of an on-chip Artificial neural network on FPGA for recognizing handwritten digits

Kumbhar, R. R., Radhika, P., & Mane, D. (2023) · International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) · doi:10.1109/raeeucci57140.2023.10134408 · Cited by 2

Picture This: A Deep Learning Model for Operational Real Estate Emissions

Gloria, B., & Höhn, B. (2023) · Journal of Sustainable Real Estate · doi:10.1080/19498276.2023.2251982 · Cited by 2

Application of Artificial Intelligence in Machine-Assisted Translation Systems

Huang, M. (2023) · International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) · doi:10.1109/easct59475.2023.10392488 · Cited by 2

Noise Profiling for ANNs: A Bio-inspired Approach

Dutta, S., Burk, J., Santer, R. D., Reyer Zwiggelaar, A., & Boongoen, T. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-47508-5_12 · Cited by 2

The screening of cannabis addiction using machine learning, MoCA, and anxiety/depression tests

Elhachimi, A., Benksim, A., Ibanni, H., & Cherkaoui, M. (2024) · Scientific African · doi:10.1016/j.sciaf.2024.e02225 · Cited by 3

Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning

Diener, R., Renz, A. W., Eckhard, F., Segbert, H., Eter, N., Malcherek, A., & Biermann, J. (2024) · Diagnostics · doi:10.3390/diagnostics14111073 · Cited by 2

Effect of Pre-processing of CT Images on the Performance of Deep Neural Networks Based Diagnosis of COVID-19

(2021) · Journal of Scientific & Industrial Research · doi:10.56042/jsir.v80i11.41310 · Cited by 2

Artificial intelligence in medicine: its working, potentials and challenges

Goel, R. (2022) · International Journal of Advances in Medicine · doi:10.18203/2349-3933.ijam20223412 · Cited by 2

Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma

Hwang, J.-H., Lim, S. H., & Park, C. K. (2025) · Information · doi:10.3390/info16030188 · Cited by 1

A deep learning AI model for determining the relationship between X-Ray detectors and patient positioning in chest radiography

Jiang, B., Hu, J., Chen, X., Wu, X., Deng, K., Yang, H., Situ, W., & Jiang, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333209 · Cited by 1

Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review

López-Medina, D. C., Oliveros, A., Moreno-Ángel, N., Arellano, A. C. H., & Henao-Pérez, M. (2026) · Journal of Medical Internet Research · doi:10.2196/78168 · Cited by 1

Ultrasonic detection of white etching defect based on convolution neural network

Zhu, Q., Xu, D., Zhang, Y.-J., Li, Y., Wang, W., & Zhang, H. (2022) · Acta Physica Sinica · doi:10.7498/aps.71.20221504 · Cited by 1

Indonesian Stock Prices Prediction using Bidirectional Long Short-Term Memory

Dinata, I. G. A., Yudistira, N., & Muflikhah, L. (2022) · International Conference on Sustainable Information Engineering and Technology (SIET) · doi:10.1145/3568231.3568249 · Cited by 1

Real-Time Emotion Recognition Using CNN and OpenCV

Tresnawati, D., Prihatmanto, A. S., Hutabarat, M. T., Yusuf, R., Lestari, N., & Zakiah, A. (2023) · doi:10.1109/iceei59426.2023.10346723 · Cited by 1

Exploring the Impact of Image Quality on Convolutional Neural Networks: A Study on Noise, Blur, and Contrast

Ghoben, M. K., & Muhammed, L. A. (2023) · International Conference of Computer Science and Information Technology (ICoSNIKOM) · doi:10.1109/icosnikom60230.2023.10364394 · Cited by 1

Detecção de sigatoka amarela em folhas de banana com redes neurais convolucionais (CNN) e aprendizagem por transferência profunda (DTL)

da Silva, V. M. O., Nogueira, T. D. C., da Cruz, G., Vinhal, C. D. N., Santana, D. D. O., & Carvalho, C. H. R. (2024) · OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA · doi:10.55905/oelv22n6-146 · Cited by 1

Artificial Intelligence Applications to Detect Pediatric Brain Tumor Biomarkers

Sadeghi, P., Ghazizadeh, Y., Arabshahi, S., Habibzadeh, A., Karimi, H., Bordbar, S., Jolfayi, A. G., & Pourbakhtyaran, E. (2024) · Interdisciplinary cancer research · doi:10.1007/16833_2024_434 · Cited by 1

Artificial intelligence and early esophageal cancer

Li, N., & Jin, S. (2021) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v2.i5.198 · Cited by 1

Application of Supervised Machine Learning Models for Drug-Action Prediction Towards Nuclear Type I ReceptorsPreprint

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