The state of the art of deep learning models in medical science and their challenges

ImageNet classification with deep convolutional neural networks

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Deep learning

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Gradient-based learning applied to document recognition

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A fast learning algorithm for deep belief nets

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Speech recognition with deep recurrent neural networks

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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

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Building a Large Annotated Corpus of English: The Penn Treebank

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Learning Deep Architectures for AI

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Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia, N. G., Kaplanoğlu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 498

Machine learning and deep learning approach for medical image analysis: diagnosis to detection

Rana, M., & Bhushan, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14305-w · Cited by 408

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

Xu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., & Liu, F. (2024) · Bioengineering · doi:10.3390/bioengineering11101034 · Cited by 393

Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021) · Journal of Food Quality · doi:10.1155/2021/4535567 · Cited by 302

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Nasser, M., & Yusof, U. K. (2023) · Diagnostics · doi:10.3390/diagnostics13010161 · Cited by 249

DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases

Tayal, A., Gupta, J., Solanki, A., Bisht, K., Nayyar, A., & Masud, M. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00769-7 · Cited by 140

Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects

Pouyanfar, N., Harofte, S. Z., Soltani, M., Siavashy, S., Asadian, E., Ghorbani‐Bidkorbeh, F., Keçili, R., & Hussain, C. M. (2022) · Trends in Environmental Analytical Chemistry · doi:10.1016/j.teac.2022.e00160 · Cited by 134

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120

Advanced Mango Leaf Disease Detection and Severity Analysis with Federated Learning and CNN

Mehta, S., Kukreja, V., & Yadav, R. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205922 · Cited by 111

A comprehensive review of community detection in graphs

Li, J., Lai, S., Shuai, Z., Tan, Y., Jia, Y., Yu, M., Song, Z., Peng, X., Xu, Z., Ni, Y., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128169 · Cited by 135

Breast cancer classification based on hybrid CNN with LSTM model

Kaddes, M., Ayid, Y. M., Elshewey, A. M., & Fouad, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-88459-6 · Cited by 98

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Harofte, S. Z., Soltani, M., Siavashy, S., & Raahemifar, K. (2022) · Small · doi:10.1002/smll.202203169 · Cited by 92

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

Zhou, J., Dong, J., Hou, H., Huang, L., & Li, J. (2024) · Lab on a Chip · doi:10.1039/d3lc01012k · Cited by 94

Lung cancer medical images classification using hybrid CNN-SVM

Saleh, A. Y., Chin, C. K., Penshie, V., & Al-Absi, H. R. H. (2021) · International Journal of Advances in Intelligent Informatics · doi:10.26555/ijain.v7i2.317 · Cited by 74

Review of applications of deep learning in veterinary diagnostics and animal health

Xiao, S., Dhand, N. K., Wang, Z., Hu, K., Thomson, P. C., House, J. K., & Khatkar, M. S. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1511522 · Cited by 86

Dense Tissue Pattern Characterization Using Deep Neural Network

Kumar, I., Kumar, A., Kumar, V. D. A., Kannan, R., Vimal, V., Singh, K. U., & Mahmud, M. (2022) · Cognitive Computation · doi:10.1007/s12559-021-09970-2 · Cited by 65

Exploring the current and prospective role of artificial intelligence in disease diagnosis

Aamir, A., Iqbal, A., Jawed, F., Ashfaque, F., Hafsa, H., Anas, Z., Oduoye, M. O., Basit, A., Ahmed, S., Rauf, S. A., et al. (2024) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000001700 · Cited by 78

Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2022) · Sensors · doi:10.3390/s22197472 · Cited by 66

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

Daradkeh, M. K., Abualigah, L., Atalla, S., & Mansoor, W. (2022) · Electronics · doi:10.3390/electronics11132066 · Cited by 60

A Study on Cervical Cancer Prediction using Various Machine Learning Approaches

Rawat, P., Bajaj, M., Mehta, S., Sharma, V., & Vats, S. (2023) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca56705.2023.10099493 · Cited by 58

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Srivastava, V., Kumar, R., Wani, M. Y., Robinson, K. M., & Ahmad, A. (2024) · Scandinavian Journal of Infectious Diseases · doi:10.1080/23744235.2024.2425712 · Cited by 63

A Survey on Medical Image Segmentation Based on Deep Learning Techniques

Moorthy, J., & Gandhi, U. D. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040117 · Cited by 55

A Comprehensive Analysis of the Effectiveness of Machine Learning Algorithms for Predicting Water Quality

Rawat, P., Bajaj, M., Sharma, V., & Vats, S. (2023) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca56705.2023.10099968 · Cited by 50

A Comprehensive Review of Advanced Deep Learning Approaches for Food Freshness Detection

Singh, R., Nickhil, C., R.Nisha, Upendar, K., Bhukya, J., & Deka, S. C. (2024) · Food Engineering Reviews · doi:10.1007/s12393-024-09385-3 · Cited by 55

Attention-based end-to-end CNN framework for content-based X-ray image retrieval

Öztürk, Ş., Alhudhaif, A., & Polat, K. (2021) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-2105-242 · Cited by 42

An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation

Aurangzeb, K., Alharthi, R. S., Haider, S. I., & Alhussein, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3217782 · Cited by 42

Deep learning for personalized health monitoring and prediction: A review

Damaševičius, R., Jagatheesaperumal, S. K., Rajesh, K. N. V. P. S., Hussain, S., Alizadehsani, R., & Górriz, J. M. (2024) · Computational Intelligence · doi:10.1111/coin.12682 · Cited by 39

A Comprehensive Overview and Analysis of Large Language Models: Trends and Challenges

Mohammed, A., & Kora, R. (2025) · IEEE Access · doi:10.1109/access.2025.3573955 · Cited by 45

Transfer learning for histopathology images: an empirical study

Aitazaz, T., Tubaishat, A., Al-Obeidat, F. N., Shah, B., Zia, T., & Tariq, S. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07516-7 · Cited by 36

Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology

LaBoone, P. A., & Marques, O. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100294 · Cited by 58

Lung Infection and Identification using Heatmap

Srivastava, S., Dhyani, N., Sharma, V., Vats, S., Yadav, S. P., Kukreja, V., & Singh, R. (2023) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic56838.2023.10140204 · Cited by 32

Automatic damaged vehicle estimator using enhanced deep learning algorithm

Qaddour, J., & Siddiqa, S. A. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200192 · Cited by 34

A zero-knowledge proof federated learning on DLT for healthcare data

Petrosino, L., Masi, L., D’Antoni, F., Merone, M., & Vollero, L. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104992 · Cited by 33

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks

Kaushal, C., Islam, M. K., Althubiti, S. A., Alenezi, F. S., & Mansour, R. F. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7935346 · Cited by 30

Big Data Analytics in Bioinformatics and Healthcare

Verma, R. K., Kishor, K., & Jha, S. K. (2024) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-2426-4.ch002 · Cited by 27

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

Jang, H., Lee, A., Kang, J. K., Song, I. H., & Lee, S. H. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i44.7687 · Cited by 29

Attentive Bi-LSTM-Based Method for Noise Suppression in Ambulatory ECG Measurements

Kumar, V., & Muduli, P. R. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3330182 · Cited by 27

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins

Lv, Z., Guo, J., & Lv, H. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3252668 · Cited by 25

A Systematic Review of Modern Approaches in Healthcare Systems for Lung Cancer Detection and Classification

Pandey, S. K., & Bhandari, A. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09940-x · Cited by 26

A hybrid software-defined networking approach for enhancing IoT cybersecurity with deep learning and blockchain in smart cities

Alotaibi, J. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01935-8 · Cited by 23

YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation

Uddin, A. H., Abu, R. M., Yang, J., Bhatti, U. A., Ku, C. S., & Por, L. Y. (2025) · Digital Health · doi:10.1177/20552076251341092 · Cited by 25

Segmentation and classification of white blood SMEAR images using modified CNN architecture

Kumar, I., & Rawat, J. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-06139-y · Cited by 21

A Survey on Human Cancer Categorization Based on Deep Learning

Ibrahim, A., Mohamed, H. K., Maher, A., & Zhang, B. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.884749 · Cited by 21

Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis

Syed, A. H., & Khan, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.854927 · Cited by 24

Predicting Breast Cancer An Evaluation of Machine Learning Approaches

Mehta, S., Rawat, P., Bajaj, M., Vats, S., Sharma, V., & Kukreja, V. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205711 · Cited by 19

MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation

Wang, Y., Xu, J., Guan, Y., Ahmad, F., Mahmood, T., & Rehman, A. U. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00787-7 · Cited by 19

Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost

Chang, W., Ji, X., Xiao, Y., Zhang, Y., Chen, B., Liu, H., & Zhou, S. (2021) · Diagnostics · doi:10.3390/diagnostics11050792 · Cited by 19

Attention-guided neural network for early dementia detection using MRS data

kherchouche, A., Ahmed, O. B., Guillevin, C., Tremblais, B., Julian, A., Fernández-Maloigne, C., & Guillevin, R. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102074 · Cited by 18

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Alves, C. L., Cury, R. G., Roster, K. I. O., Pineda, A. M., Rodrigues, F. A., Thielemann, C., & Ciba, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277257 · Cited by 17

Human attention detection system using deep learning and brain–computer interface

Nair, S. A. L., & Megalingam, R. K. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09628-8 · Cited by 17

Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis

Hajikarimloo, B., Habibi, M. A., Alvani, M. S., Meinagh, S. O., Kooshki, A., Afkhami-Ardakani, O., Rasouli, F., Tos, S. M., Tavanaei, R., Akhlaghpasand, M., et al. (2024) · Neurological Sciences · doi:10.1007/s10072-024-07879-w · Cited by 17

Machine learning in bioinformatics

Kumar, I., Singh, S. P., & Shivam, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89775-4.00020-1 · Cited by 16

Investigation of VLLC-OCDM System for Intelligent Transport Applications

Arya, V., Kumari, M., Sharma, N. V., & Singh, P. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10270601 · Cited by 15

A Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms

Kumar, A., Sharma, N. V., Chauhan, R. S., Khare, A., Anand, A., & Sharma, M. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-53085-2_8 · Cited by 14

A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans

Liu, W., Zhang, J., & Zhao, Y. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00154 · Cited by 13

Federated learning and differential privacy in clinical health: Extensive survey

Odera, D. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0113 · Cited by 14

Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging

Maguluri, G. N., Grimble, J. P., Caron, A., Zhu, G., Krishnamurthy, S., McWatters, A., Beamer, G., Lee, S., & Iftimia, N. V. (2023) · Diagnostics · doi:10.3390/diagnostics13132276 · Cited by 13

A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms

Mathur, P., Shaikh, H., Sheth, F., Kumar, D., & Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3543475 · Cited by 13

Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation

Christinal, H. A., Hephzibah, R., Kowsalya, G., Jayanthi, R., & Chandy, D. A. (2022) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405618666220407112825 · Cited by 13

Deep clustering framework review using multicriteria evaluation

Ros, F., Riad, R., & Guillaume, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111315 · Cited by 14

Advancements in automated classification of chronic obstructive pulmonary disease based on computed tomography imaging features through deep learning approaches

Zhu, Z. (2024) · Respiratory Medicine · doi:10.1016/j.rmed.2024.107809 · Cited by 14

Machine Learning Approach to Predict Asthma Prevalence with Decision Trees

Mahammad, A. B., & Kumar, R. (2022) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs56270.2022.9988210 · Cited by 12

Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation

Das, R., Bose, S., Chowdhury, R. S., & Maulik, U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107182 · Cited by 12

Deep learning in nuclear medicine: from imaging to therapy

Zhang, M.-X., Liu, P., Zhang, M.-D., Su, P., Shang, H.-S., Zhu, J., Wang, D., Ji, X., & Liao, Q.-M. (2025) · Annals of Nuclear Medicine · doi:10.1007/s12149-025-02031-w · Cited by 14

Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction

Jassim, O. A., Abed, M. J., & Saied, Z. H. S. (2023) · Baghdad Science Journal · doi:10.21123/bsj.2023.8340 · Cited by 11

Recapitulating the Cancer‐Immunity Cycle on a Chip

Lee, Y., Min, J., Kim, S., Park, W., Ko, J., & Jeon, N. L. (2024) · Advanced Healthcare Materials · doi:10.1002/adhm.202401927 · Cited by 10

Accurate bladder cancer diagnosis using ensemble deep leaning

El-Atier, R. A., Saraya, M. S., Saleh, A. I., & Rabie, A. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-95002-0 · Cited by 10

Abnormal behavior identification of enterprise cloud platform financial system based on artificial neural network

Wang, Y. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109110 · Cited by 10

Secure Optical Wireless Satellite Network based OWC-OCDM System with DDW code

Arya, V., Kumari, M., Chauhan, R. S., & Sharma, N. V. (2023) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat59970.2023.10353493 · Cited by 9

Machine Learning Techniques for Data-Driven Computer-Aided Diagnostic Method of Polycystic Ovary Syndrome (PCOS) resulting from Functional Ovarian Hyperandrogenism (FOH)

Batra, H., Saluja, K., Gupta, S., Kaushal, R. K., Sharma, N. V., & Singh, P. (2023) · International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) · doi:10.1109/cises58720.2023.10183485 · Cited by 9

Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation

Kurian, N. C., Lehan, A., Verghese, G. E., Dharamshi, N., Meena, S., Li, M., Liu, F., Gillet, C., Rane, S. U., Grigoriadis, A. E., et al. (2022) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe55377.2022.00027 · Cited by 9

Convolutional neural network–multi-kernel radial basis function neural network–salp swarm algorithm: a new machine learning model for predicting effluent quality parameters

Khozani, Z. S., Ehteram, M., Mohtar, W. H. M. W., Achite, M., & Chau, K. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-29406-8 · Cited by 8

Artificial intelligence for digital healthcare in the low and medium income countries

Sibiya, S. S., Hurchund, R., Omondi, B. O., & Owira, P. M. O. (2025) · Health and Technology · doi:10.1007/s12553-025-00950-2 · Cited by 10

Empowering photodynamic therapy with artificial intelligence: current trends and future directions

Paul, A., Xavierselvan, M., Aebisher, D., Kubrak, T. P., Bartusik‐Aebisher, D., & Mallidi, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1771804 · Cited by 8

MULTIPLE DISEASE PREDICTION SYSTEM USING MACHINE LEARNING

(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets35252 · Cited by 8

A hybrid deep learning model in predicting weather temperature

Yasavoli, B., Habibirad, A., & Javanshiri, Z. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01963-1 · Cited by 9

Deep Learning‐Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging

Zhuo, M., Chen, X., Guo, J., Qian, Q., Xue, E., & Chen, Z. (2024) · Journal of Ultrasound in Medicine · doi:10.1002/jum.16489 · Cited by 7

Technology at the Table: Incorporating AI into Contemporary Food Industry Operations

Kataria, P. (2024) · Food Science and Nutrition Cases · doi:10.1079/foodsciencecases.2024.0003 · Cited by 7

Social robot assisted music course based on speech sensing and deep learning algorithms

Xiao, D. (2024) · Entertainment Computing · doi:10.1016/j.entcom.2024.100814 · Cited by 8

Hybrid Approach to Train Delay Prediction: An Integration of Analytical Model and Deep Learning Techniques

Wu, X., Lian, W., Zhou, M., Dong, H., & Fang, F. (2024) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2024.3440505 · Cited by 7

CBAM Attention Gate‐Based Lightweight Deep Neural Network Model for Improved Retinal Vessel Segmentation

Fareed, K., Khan, A. A., Alhussein, M. A., Aurangzeb, K., Shahzad, A., & Islam, M. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70031 · Cited by 6

Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning

Li, T., Liao, M., Fu, Y. M., Zhang, F., Shen, L., Che, J., Wu, S., Liu, J., Wu, W., He, P., et al. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01641-0 · Cited by 6

Recent Challenges in Medical Science using Machine Learning Techniques: A Review

Shah, M., Padhiyar, P., Parmar, K., Patel, J. A., Panesar, S. F., & Raja, K. (2023) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac58438.2023.10290424 · Cited by 6

The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++

Wang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-01949-5 · Cited by 6

Design of Semiautomatic Digital Creation System for Electronic Music Based on Recurrent Neural Network

Duan, Y., & Wang, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5457376 · Cited by 6

La salud en la era digital

Gutiérrez, C., & López, M. N. (2022) · Revista Médica Clínica Las Condes · doi:10.1016/j.rmclc.2022.11.001 · Cited by 6

Deep Learning Research Directions in Medical Imaging

Simionescu, C., & Iftene, A. (2022) · Mathematics · doi:10.3390/math10234472 · Cited by 6

Evaluating the transferability of machine-learned force fields for material property modeling

Mohanty, S., Yoo, S., Kang, K., & Wei Cai, A. (2023) · Computer Physics Communications · doi:10.1016/j.cpc.2023.108723 · Cited by 6

A Review on the Impacts of Artificial Intelligence (AI) on Youth

Mukhopadhyay, S., & Chakrabarti, A. (2023) · Springer eBooks · doi:10.1007/978-981-99-4969-4_11 · Cited by 5

Imaging update in spondyloarthropathy

Shah, A., Raja, N. R., & Rennie, W. J. (2021) · Journal of Clinical Orthopaedics and Trauma · doi:10.1016/j.jcot.2021.101564 · Cited by 6

Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network

Jerković, I., Bašić, Ž., & Kružić, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-07608-z · Cited by 5

A Survey of Large-Scale Deep Learning Models in Medicine and Healthcare

Chen, Z., Liu, R., Huang, S., Guo, Y., & Ren, Y. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.067809 · Cited by 6

Self-supervised learning for breast cancer detection: A review

Figueiras, H., Domingues, J., Matela, N., & Garcia, N. C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111245 · Cited by 5

On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

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