The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: A narrative review

UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · see citing works

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works

Emerging Properties in Self-Supervised Vision Transformers

Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638 · see citing works

mixOmics: An R package for ‘omics feature selection and multiple data integration

Rohart, F., Gautier, B., Singh, A., & Lê Cao, K.-A. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005752 · Cited by 4,056 · see citing works

MIMIC-IV, a freely accessible electronic health record dataset

Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01899-x · Cited by 3,401 · see citing works

Foundation models for generalist medical artificial intelligence

Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · cited 1× · see citing works

Deep learning-enabled medical computer vision

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · see citing works

Multimodal biomedical AI

Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works

DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M. A., Tebbutt, S. J., & Cao, K. L. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1054 · Cited by 1,079 · see citing works

Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · cited 1× · see citing works

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01595-0 · Cited by 928 · see citing works

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842 · see citing works

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

Yala, A., Lehman, C., Schuster, T., Portnoi, T., & Barzilay, R. (2019) · Radiology · doi:10.1148/radiol.2019182716 · Cited by 758 · see citing works

Integrating Multimodal Information in Large Pretrained Transformers

Rahman, W., Hasan, M. K., Lee, S., Zadeh, A. B., Mao, C., Morency, L., & Hoque, M. E. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.214 · Cited by 743 · see citing works

Machine learning for medical imaging: methodological failures and recommendations for the future

Varoquaux, G. P., & Cheplygina, V. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00592-y · Cited by 720 · see citing works

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond

Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

Khemani, B., Patil, S., Kotecha, K. V., & Tanwar, S. (2024) · Journal Of Big Data · doi:10.1186/s40537-023-00876-4 · Cited by 638 · see citing works

BEHRT: Transformer for Electronic Health Records

Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62922-y · Cited by 595 · see citing works

Seven-point checklist and skin lesion classification using multitask multimodal neural nets

Kawahara, J., Daneshvar, S., Argenziano, G., & Hamarneh, G. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2824327 · Cited by 584 · see citing works

Untitled work

(2016) · PubMed · Cited by 576 · see citing works

Multimodal machine learning in precision health: A scoping review

Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · cited 2× · see citing works

Graph Neural Networks and Their Current Applications in Bioinformatics

Zhang, X., Li, L., Liu, L., & Tang, M. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.690049 · Cited by 413 · see citing works

Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer

Vanguri, R. S., Luo, J., Aukerman, A. T., Egger, J. V., Fong, C. J., Horvat, N., Pagano, A., Araujo-Filho, J. D. A. B., Geneslaw, L., Rizvi, H., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00416-8 · Cited by 395 · see citing works

Integrated multimodal artificial intelligence framework for healthcare applications

Soenksen, L. R., Ma, Y., Zeng, C., Boussioux, L., Villalobos Carballo, K., Na, L., Wiberg, H. M., Li, M. L., Fuentes, I., & Bertsimas, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00689-4 · Cited by 380 · cited 3× · see citing works

Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer

Boehm, K. M., Aherne, E. A., Ellenson, L., Nikolovski, I., Alghamdi, M., Vázquez-García, I., Zamarin, D., Long Roche, K., Liu, Y., Patel, D., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00388-9 · Cited by 357 · see citing works

A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics

Zhou, H.-Y., Yu, Y., Wang, C., Zhang, S., Gao, Y., Pan, J., Shao, J., Lu, G., Zhang, K., & Li, W. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01045-x · Cited by 343 · see citing works

Multimodal skin lesion classification using deep learning

Yap, J., Yolland, W., & Tschandl, P. (2018) · Experimental Dermatology · doi:10.1111/exd.13777 · Cited by 326 · see citing works

Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints

Oren, O., Gersh, B. J., & Bhatt, D. L. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30160-6 · Cited by 317 · see citing works

Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare

Jeyaraman, M., Balaji, S., Jeyaraman, N., & Yadav, S. (2023) · Cureus · doi:10.7759/cureus.43262 · Cited by 297 · see citing works

Graph representation learning in biomedicine and healthcare

Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · cited 1× · see citing works

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S. S., Weiske, J., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3604 · Cited by 275 · see citing works

Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection

Huang, S.-C., Pareek, A., Zamanian, R., Banerjee, I., & Lungren, M. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-78888-w · Cited by 246 · see citing works

A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges

Nazir, A., & Wang, Z. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100022 · Cited by 245 · cited 1× · see citing works

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials

Esteva, A., Feng, J., van der Wal, D., Huang, S.-C., Simko, J. P., DeVries, S., Chen, E., Schaeffer, E. M., Morgan, T. M., Sun, Y., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00613-w · Cited by 217 · see citing works

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

Nie, D., Lu, J., Zhang, H., Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., & Shen, D. (2019) · Scientific Reports · doi:10.1038/s41598-018-37387-9 · Cited by 213 · see citing works

Author Correction: MIMIC-IV, a freely accessible electronic health record dataset

Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T., Moody, B., Gow, B. J., Lehman, L.-W. H., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-01945-2 · Cited by 203 · see citing works

Latent bias and the implementation of artificial intelligence in medicine

DeCamp, M., & Lindvall, C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa094 · Cited by 198 · see citing works

Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning

Brugnara, G., Neuberger, U., Mahmutoglu, M. A., Foltyn‐Dumitru, M., Herweh, C., Nagel, S., Schönenberger, S., Heiland, S., Ulfert, C., Ringleb, P. A., et al. (2020) · Stroke · doi:10.1161/strokeaha.120.030287 · Cited by 194 · see citing works

xMWAS: a data-driven integration and differential network analysis tool

Uppal, K., Ma, C., Go, Y., & Jones, D. P. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx656 · Cited by 185 · see citing works

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · cited 2× · see citing works

An adversarial training framework for mitigating algorithmic biases in clinical machine learning

Yang, J., Soltan, A. A. S., Eyre, D. W., Yang, Y., & Clifton, D. A. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00805-y · Cited by 159 · see citing works

TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records

Yang, Z., Mitra, A., Liu, W., Berlowitz, D., & Yu, H. (2023) · Nature Communications · doi:10.1038/s41467-023-43715-z · Cited by 158 · see citing works

2020 ACR Data Science Institute Artificial Intelligence Survey

Allen, B., Agarwal, S., Coombs, L. P., Wald, C., & Dreyer, K. J. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2021.04.002 · Cited by 154 · see citing works

Security Implications of AI Chatbots in Health Care

Li, J. (2023) · Journal of Medical Internet Research · doi:10.2196/47551 · Cited by 148 · see citing works

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas

Zhou, H., Chang, K., Bai, H. X., Xiao, B., Su, C., Bi, W. L., Zhang, P. J., Senders, J. T., Vallières, M., Kavouridis, V. K., et al. (2019) · Journal of Neuro-Oncology · doi:10.1007/s11060-019-03096-0 · Cited by 130 · see citing works

Multimodal Deep Learning for Prognosis Prediction in Renal Cancer

Schulz, S., Woerl, A.-C., Jungmann, F., Glasner, C., Stenzel, P., Strobl, S., Fernandez, A., Wagner, D., Haferkamp, A., Mildenberger, P., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.788740 · Cited by 123 · see citing works

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Rajpurkar, P., Park, A., Irvin, J., Chute, C. G., Bereket, M. D., Mastrodicasa, D., Langlotz, C. P., Lungren, M. P., Ng, A. Y., & Patel, B. N. (2020) · Scientific Reports · doi:10.1038/s41598-020-61055-6 · Cited by 122 · see citing works

Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

Steyaert, S., Qiu, Y. L., Zheng, Y., Mukherjee, P., Vogel, H. S., & Gevaert, O. (2023) · Communications Medicine · doi:10.1038/s43856-023-00276-y · Cited by 115 · see citing works

Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers

Khader, F., Müller-Franzes, G., Wang, T., Han, T., Tayebi Arasteh, S., Haarburger, C., Stegmaier, J., Bressem, K., Kuhl, C., Nebelung, S., et al. (2023) · Radiology · doi:10.1148/radiol.230806 · Cited by 107 · see citing works

Multimodal manifold-regularized transfer learning for MCI conversion prediction

Initiative, A. D. N., Cheng, B., Liu, M., Suk, H., Shen, D. G., & Zhang, D. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9356-x · Cited by 102 · see citing works

Cascaded multi-modal mixing transformers for Alzheimer’s disease classification with incomplete data

Liu, L., Liu, S., Zhang, L., To, X. V., Nasrallah, F., & Chandra, S. S. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120267 · Cited by 101 · see citing works

The EMory BrEast imaging Dataset (EMBED): a racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images

Jeong, J. J., Vey, B. L., Bhimireddy, A., Kim, T., Santos, T., Correa, R., Dutt, R., Mosunjac, M., Oprea-Ilies, G., Smith, G., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.220047 · Cited by 92 · see citing works

A multimodal graph neural network framework for cancer molecular subtype classification

Li, B., & Nabavi, S. (2024) · BMC Bioinformatics · doi:10.1186/s12859-023-05622-4 · Cited by 88 · see citing works

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques

Kumar, S., Chaube, M. K., Alsamhi, S. H., Gupta, S. K., Guizani, M., Gravina, R., & Fortino, G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107109 · Cited by 86 · see citing works

Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks

Mehrtash, A., Sedghi, A., Ghafoorian, M., Taghipour, M., Tempany, C. M., Wells, W. M., Kapur, T., Mousavi, P., Abolmaesumi, P., & Fedorov, A. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2277123 · Cited by 85 · see citing works

A transformer-based unified multimodal framework for Alzheimer's disease assessment

Yu, Q., Ma, Q., Da, L., Li, J., Wang, M., Xu, A., Li, Z., & Li, W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108979 · Cited by 82 · see citing works

Graph neural networks for clinical risk prediction based on electronic health records: A survey

Boll, H. O., Amirahmadi, A., Ghazani, M. M., de Morais, W. O., de Freitas, E. P., Soliman, A., Etminani, F., Byttner, S., & Recamonde‐Mendoza, M. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104616 · Cited by 81 · see citing works

MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events

Lyu, T., Gao, J., Tian, L., Li, Z., Zhang, P., & Zhang, J. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/487 · Cited by 80 · see citing works

Transformer based on channel-spatial attention for accurate classification of scenes in remote sensing image

Guo, J., Jia, N., Zhang, M., Zhang, R., & Li, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-19831-z · Cited by 79 · see citing works

Graph Neural Networks for Multimodal Single-Cell Data Integration

Wen, H., Ding, J., Jin, W., Wang, Y., Xie, Y., & Tang, J. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539213 · Cited by 77 · see citing works

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

Yao, Y., Lv, Y., Tong, L., Liang, Y., Xi, S., Ji, B., Zhang, G., Li, L., Tian, G., Tang, M., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac448 · Cited by 74 · see citing works

A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer’s Disease

Spasov, S. E., Passamonti, L., Duggento, A., Lio, P., & Toschi, N. (2018) · 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512468 · Cited by 73 · see citing works

A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging

Mehralivand, S., Yang, D., Harmon, S. A., Xu, D., Xu, Z., Roth, H. R., Masoudi, S., Sanford, T., Kesani, D., Lay, N. S., et al. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.08.019 · Cited by 73 · see citing works

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

Bleker, J., Kwee, T. C., Dierckx, R. A. J. O., de Jong, I. J., Huisman, H. J., & Yakar, D. (2019) · European Radiology · doi:10.1007/s00330-019-06488-y · Cited by 68 · see citing works

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Peeken, J. C., Goldberg, T., Pyka, T., Bernhofer, M., Wiestler, B., Kessel, K. A., Tafti, P. D., Nüsslin, F., Braun, A. E., Zimmer, C., et al. (2018) · Cancer Medicine · doi:10.1002/cam4.1908 · Cited by 67 · see citing works

Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data

Khader, F., Kather, J. N., Müller-Franzes, G., Wang, T., Han, T., Tayebi Arasteh, S., Hamesch, K., Bressem, K., Haarburger, C., Stegmaier, J., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-37835-1 · Cited by 57 · see citing works

Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network

Gao, J., Lyu, T., Xiong, F., Wang, J., Ke, W., & Li, Z. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3083566 · Cited by 54 · see citing works

A multi-modal deep neural network for multi-class liver cancer diagnosis

Khan, R. A., Fu, M., Burbridge, B., Luo, Y., & Wu, F. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.06.013 · Cited by 54 · see citing works

Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images

Dong, Y., Hou, L., Yang, W., Han, J., Wang, J., Qiang, Y., Zhao, J., Hou, J., Song, K., Ma, Y., et al. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-600 · Cited by 52 · see citing works

Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care

Chandran, U., Reps, J., Robert Che-An, Y. A. N. G., Vachani, A., Maldonado, F., & Kalsekar, I. D. (2022) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-22-0873 · Cited by 52 · see citing works

Machine‐learning radiomics to predict early recurrence in perihilar cholangiocarcinoma after curative resection

Huan, Q., Hu, X., Zhang, J., Dai, H., He, Y., Zhao, Z., Yang, J., Xu, Z., Hu, X., & Chen, Z. (2020) · Liver International · doi:10.1111/liv.14763 · Cited by 48 · see citing works

Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours

Lu, G., Tian, R., Yang, W. P., Liu, R., Liu, D., Xiang, Z., & Zhang, G. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1402967 · Cited by 46 · see citing works

Predicting Rehospitalization within 2 Years of Initial Patient Admission for a Major Depressive Episode: A Multimodal Machine Learning Approach

Cearns, M., Opel, N., Clark, S. R., Flint, C., Thalamuthu, A., Heindel, W. L., Winter, T., Teismann, H., Minnerup, H., Dannlowski, U., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0615-2 · Cited by 45 · see citing works

Multimodal Fusion of Imaging and Genomics for Lung Cancer Recurrence Prediction

Subramanian, V., Do, M. N., & Syeda-Mahmood, T. (2020) · 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098545 · Cited by 42 · see citing works

A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans

Chen, Q., Xu, X., Hu, S., Xiao, L., Zou, Q., & Li, Y. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2279021 · Cited by 41 · see citing works

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data

Nguyen, H. H., Blaschko, M. B., Saarakkala, S. S., & Tiulpin, A. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3312524 · Cited by 41 · see citing works

Lightweight transformers for clinical natural language processing

Rohanian, O., Nouriborji, M., Jauncey, H., Kouchaki, S., Nooralahzadeh, F., Clifton, L., Merson, L., & Clifton, D. A. (2024) · Natural Language Engineering · doi:10.1017/s1351324923000542 · Cited by 41 · see citing works

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes

Zhu, J., Oh, J. H., Simhal, A. K., Elkin, R., Norton, L., Deasy, J. O., & Tannenbaum, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107117 · Cited by 39 · see citing works

Clinical applications of graph neural networks in computational histopathology: A review

Meng, X., & Zou, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107201 · Cited by 38 · see citing works

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes

Huang, Y., Chen, C., & Yang, H. (2024) · Nature Communications · doi:10.1038/s41467-024-48618-1 · Cited by 37 · see citing works

Multi-transSP: Multimodal Transformer for Survival Prediction of Nasopharyngeal Carcinoma Patients

Zheng, H., Lin, Z., Zhou, Q., Peng, X., Xiao, J., Zu, C., Jiao, Z., & Wang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1_23 · Cited by 34 · see citing works

Early neural network development history: the age of Camelot

Eberhart, R. C., & Dobbins, R. W. (1990) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.59207 · Cited by 33 · see citing works

ChatGPT Surpasses 1000 Publications on PubMed: Envisioning the Road Ahead

Temsah, M.-H. A., Altamimi, I., Jamal, A. A., Alhasan, K. A., & Al-Eyadhy, A. A. (2023) · Cureus · doi:10.7759/cureus.44769 · Cited by 33 · see citing works

Co-embedding of edges and nodes with deep graph convolutional neural networks

Zhou, Y., Huo, H., Hou, Z., Bu, L., Mao, J., Wang, Y., Lv, X., & Bu, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-44224-1 · Cited by 33 · see citing works

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study

Lai, J., Chen, Z., Liu, J., Zhu, C., Huang, H., Yi, Y., Cai, G., & Liao, N. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001082 · Cited by 32 · see citing works

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer

Hu, C., Qiao, X., Huang, R., Hu, C., Bao, J., & Wang, X. (2024) · Radiology Imaging Cancer · doi:10.1148/rycan.230143 · Cited by 30 · see citing works

Multimodal graph attention network for COVID-19 outcome prediction

Keicher, M., Burwinkel, H., Bani-Harouni, D., Paschali, M., Czempiel, T., Burian, E., Makowski, M. R., Braren, R., Navab, N., & Wendler, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-46625-8 · Cited by 27 · see citing works

Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images

Nai, Y., Teo, B. W., Tan, N. L., Chua, K. Y. W., Wong, C. K., O’Doherty, S., Stephenson, M. C., Schaefferkoetter, J. D., Thian, Y. L., Chiong, E., et al. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/8861035 · Cited by 24 · see citing works

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification

Simon, B., Merriman, K. M., Harmon, S. A., Tetreault, J., Yılmaz, E. C., Blake, Z., Merino, M. J., An, J. Y., Marko, J., Law, Y. M., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.04.011 · Cited by 24 · see citing works

Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI

Janßen, C., Boskamp, T., Arrastia, J. L., Baguer, D. O., Hauberg‐Lotte, L., Kriegsmann, M., Kriegsmann, K., Steinbuß, G., Casadonte, R., Kriegsmann, J., et al. (2022) · Cancers · doi:10.3390/cancers14246181 · Cited by 23 · see citing works

Feature Ranking Importance from Multimodal Radiomic Texture Features using Machine Learning Paradigm: A Biomarker to Predict the Lung Cancer

Shim, S., Alkinani, M. H., Hussain, L., & Aziz, W. (2022) · Big Data Research · doi:10.1016/j.bdr.2022.100331 · Cited by 22 · see citing works

The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical Records

Baxter, R., Nind, T., Sutherland, J., McAllister, G., Hardy, D. R., Hume, A. C., MacLeod, R., Caldwell, J., Krueger, S., Tramma, L., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.220266 · Cited by 18 · see citing works

Multimodal Contrastive Supervised Learning to Classify Clinical Significance MRI Regions on Prostate Cancer

Gutiérrez, Y., Arévalo, J., & Martínez, F. (2022) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc48229.2022.9871243 · Cited by 14 · see citing works

Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network

Ding, M., Cui, H., Li, B., Zou, B., Fan, B., Ma, L., Wang, Z., Li, W., Yu, J., & Wang, L. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2022.12.050 · Cited by 11 · see citing works

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype

Alam, F., Ananbeh, O., Malik, K. M., Odayani, A. A., Hussain, I. B., Kaabia, N. M., Aidaroos, A. Y. A., & Saudagar, A. K. J. (2023) · Diagnostics · doi:10.3390/diagnostics13101760 · Cited by 8 · see citing works

Author Correction: Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials

Esteva, A., Feng, J., van der Wal, D., Huang, S.-C., Simko, J., DeVries, S., Chen, E., Schaeffer, E. M., Morgan, T. M., Sun, Y., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00769-z · Cited by 5 · see citing works

Multimodal Artificial Intelligence in Medical Diagnostics

Jandoubi, B., & Akhloufi, M. A. (2025) · Information · doi:10.3390/info16070591 · Cited by 88 · see citing works

Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues

Ali, M. U., Benfante, V., Basirinia, G., Alongi, P., Sperandeo, A., Quattrocchi, A., Giannone, A. G., Cabibi, D., Yezzi, A. J., Raimondo, D. D., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11020059 · Cited by 73 · see citing works

The Impact of Modern AI in Metadata Management

Yang, W., Fu, R., Amin, M. B., & Kang, B. (2025) · Human-Centric Intelligent Systems · doi:10.1007/s44230-025-00106-5 · Cited by 43 · see citing works

Diagnostic Performance of ChatGPT‐4o and DeepSeek‐3 Differential Diagnosis of Complex Oral Lesions: A Multimodal Imaging and Case Difficulty Analysis

Hassanein, F. E. A., Barbary, A. E., Hussein, R. R., Ahmed, Y., Elguindy, J., Sarhan, S., & Abou‐Bakr, A. (2025) · Oral Diseases · doi:10.1111/odi.70007 · Cited by 28 · see citing works

Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective

d’Amati, A., Baldini, G. M., Difonzo, T., Santoro, A., Dellino, M., Cazzato, G., Malvası, A., Vimercati, A., Resta, L., Zannoni, G. F., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11040110 · Cited by 9 · see citing works

Exploring the Role of AI in Predicting Chronic Disease Progression: Diabetes and Cardiovascular Diseases

Fahmy, A. (2025) · Premier Journal of Public Health · doi:10.70389/pjph.100021 · Cited by 3 · see citing works

Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

Matsumoto, K., Suzuki, M., Ishihara, K., Tokunaga, K., Matsuda, K., Chen, J., Yamashiro, S., Soejima, H., Nakashima, N., & Kamouchi, M. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105989 · Cited by 4 · see citing works

Genetic and epigenetic drivers of neurodegenerative disorders

Roy, K., Syed, M., Narad, P., & Sengupta, A. (2025) · Progress in brain research · doi:10.1016/bs.pbr.2025.08.002 · Cited by 5 · see citing works

Preparation of intervertebral disc organoid and regeneration mechanism based on gradient structure and mechanical function guidance: A Review

Haque, M. A., Li, J., Gao, M., & Zhou, Z. (2025) · Advanced Orthopaedics · doi:10.1016/j.advop.2025.10.001 · Cited by 5 · see citing works

From diagnosis and treatment to prognosis: Clinical prospects of artificial intelligence in multimodal research of hepatocellular carcinoma

Jia, W., Duan, X., Yao, Q., Liu, R., & Cheng, C. L. (2025) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105102 · Cited by 4 · see citing works

Can ChatGPT Aid in Musculoskeletal Intervention?

Shazahan, M. A., Pellakuru, S. R., Saran, S., Chapala, S., Mettu, S., & Botchu, R. (2025) · Journal of Clinical Interventional Radiology ISVIR · doi:10.1055/s-0045-1809953 · Cited by 3 · see citing works

MedTransNet: a transformer-based clinical decision support framework for early infectious disease prognosis and antimicrobial stewardship

Youssef, Y. B., Amdouni, A., Manita, G., & Hassine, M. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00653-8 · Cited by 4 · see citing works

Oral microbiome dysbiosis in autism spectrum disorder: the oral-gut-brain axis and future perspectives: a narrative review

Huang, Y., Liang, Q., Youjin, S., Chen, J., & Xu, W. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1783810 · Cited by 3 · see citing works

Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review

Yang, M., Shi, N., Chen, H., Po, E. Y. P., Ng, P. Y., Lai, P. C. K., Wu, Y.-C., & Fong, D. Y. T. (2026) · Health Data Science · doi:10.34133/hds.0356 · Cited by 3 · see citing works

Generative AI and Scientific Authorship

Hsieh, S.-C., Lee, K.-H., Karmakar, R., Kandalkar, A., Mukundan, A., & Wang, H. (2025) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.394323 · Cited by 2 · see citing works

Impact of AI-assisted decision support on radiological diagnosis of jawbone lesions

Berne, J. V., Saadi, S. B., Andrade‐Bortoletto, M. F. S., Fontenele, R. C., & Jacobs, R. (2026) · Clinical Oral Investigations · doi:10.1007/s00784-026-06745-7 · Cited by 2 · see citing works

A multicenter explainable machine learning analysis of autoimmune disease comorbidity in ankylosing spondylitis

Zhu, J., Huang, C., Lin, Y., Chen, T., Ren, L., Chen, J., Xue, J., Li, H., Cheng, H., Zhan, X., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1775877 · Cited by 2 · see citing works

Applications of Artificial Intelligence in Cancer Diagnosis and Treatment

Xie, Y., Wang, Z., Zeng, Z., Xin, T., Yuan, S., Qin, F., & Chen, Z. (2026) · Cancer Medicine · doi:10.1002/cam4.72143 · Cited by 2 · see citing works

Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging

Guarrasi, V., Mogensen, K., Tassinari, S., Qvarlander, S., & Soda, P. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227201 · Cited by 2 · see citing works

Multimodal Retrieval Method for Images and Diagnostic Reports Using Cross-Attention

Sata, I., Amagasaki, M., & Kiyama, M. (2025) · AI · doi:10.3390/ai6020038 · Cited by 3 · see citing works

Translationale Herausforderungen und klinisches Potenzial von künstlicher Intelligenz in der minimal-invasiven Chirurgie

Carstens, M., Pfeiffer, M., Speidel, S., Distler, M., Weitz, J., & Kolbinger, F. R. (2025) · Die Chirurgie · doi:10.1007/s00104-025-02366-0 · Cited by 1 · see citing works

Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine

Marchesi, R., Lazzaro, N., Endrizzi, W., Leonardi, G., Pozzi, M., Ragni, F., Bovo, S., Moroni, M., Osmani, V., & Jurman, G. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013455 · Cited by 1 · see citing works

Artificial intelligence in MRI clinical practice: From historical innovation to emerging trends

Aldahery, S. T. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102158 · Cited by 1 · see citing works

Computed tomography of interstitial lung disease in systemic sclerosis: dataset and deep learning model for pulmonary lesion segmentation

Vahdani, A. M., Mohamadi, A., Nayebirad, S., Forghani, S., Karimi, E., Vojgani, Y., Tamartash, Z., Asadi, M., Aghaghazvini, L., Gharibdoost, F., et al. (2026) · Reumatismo · doi:10.4081/reumatismo.2026.1920 · Cited by 1 · see citing works

3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction

Gao, Q., Wu, J., Gao, Y., Ren, Y., Wang, X., Zhu, G., Xiang, J., An, D., Xu, L., Zhou, Y., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02449-0 · Cited by 1 · see citing works

Deep Learning in Dental Radiology

Shaikh, A., & Prasanna, D. V. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-4114-9.ch007 · Cited by 1 · see citing works

Artificial intelligence in orthopaedics: Clinical decision support, medical imaging, surgical planning, and outcome prediction

Dwajan, A., PATRO, D. R., Agarwal, A., & Lalhmingmawii, M. (2026) · World Journal of Clinical Cases · doi:10.12998/wjcc.v14.i17.120192 · Cited by 1 · see citing works

The Impact of Artificial Intelligence on Contemporary Hospital Systems

Rajeswari, D., Amala, K. J., & Venkatachalam, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2787-7.ch001

Assessing the Reliability of Large Language Models in Radiological Scenarios

Gouri, I., Dehbi, R., & Dehbi, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8011-7.ch009

Editorial: Novel anti-cancer drugs combination radio-immunotherapy strategy: new frontiers in cancer immunotherapy

Burrow, W., Chang, J., Farooqı, A. A., Huang, S., & Tao, W. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1747813

Multimodal artificial intelligence integrates imaging, endoscopic, and omics data for intelligent decision-making in individualized gastrointestinal tumor treatment

Nian, H., Wu, Y.-B., Bai, Y., Zhang, Z.-L., Tu, X., Liu, Q., Zhou, D.-H., & Du, Q. (2026) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v7.i1.115498

SGSP-MRI: Segmentation-Guided Survival Prediction from Multimodal MRI in Nasopharyngeal Carcinoma

Long, Y., Huang, G., Cheng, L., Ma, X., Li, Z., Chen, X., Xie, S., Zou, J., & Li, Z. (2025) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1109/icsps66615.2025.11347944

Early Prognosis of Post-Covid Cardiovascular Disease with Clinical Data and Chest X-Ray Image Using Transformer- Based Approach

Juliet, S. D., Banumathi, J., & Singh, J. A. J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13029-7_28

Patient-Centric Statistical Multi-Modal Fusion for Medical Diagnosis: Integrating DICOM, Radiomics, and Patient Attributes

Choi, S.-Y., & Lee, K. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00238

Multimodal artificial intelligence in capsule endoscopy: Integrating video and sensor data for advanced gastrointestinal diagnostics

Chowdhary, R., Sheth, P. D., Rampurawala, I. M., Kapadia, C., Vohra, C., Chowdhary, R., Arora, K., Taranikanti, V., Vuthaluru, A. R., Goyal, O., et al. (2026) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v7.i1.117988

Exploring AI in radiology: a bibliometric study tailored to a research path

Gouri, I., Dehbi, R., Dehbi, A., & Batouta, Z. I. (2026) · Die Radiologie · doi:10.1007/s00117-026-01579-6

Meta learning optimized TabNet for small sample repeat prostate biopsy prediction

Lou, J., Xu, J., Mao, D., Zhao, Y., Ye, F., & Wu, M. (2026) · Discover Oncology · doi:10.1007/s12672-026-04777-9

Multimodal Generative AI for Next-Generation Healthcare Diagnostics and Predictive Analytics

Tummuri, S. S. R. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468232

A Combination of Human Wisdom and Artificial Intelligence will Win the Game

He, L., Harold, S. F. (., & Frydrysiak-Brzozowska, A. (2026) · Newborn · doi:10.5005/newborn-5-1-iv

Vision Language Models in Healthcare Through a Multimodal Approach to Medical Imaging and Clinical Applications

Tanvir, K., Biswas, R., Gomes, D., Rahman, M. M., Dey, N., & Islam, M. R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7315-7.ch009

Fusion Intelligence for Anemia Diagnosis: Multimodal AI Approaches for Early Detection and Integrated Management

Singh, R. K., Singh, R. R., Singh, A., Pandey, J. P., & Gautam, A. K. (2026) · Springer eBooks · doi:10.1007/978-981-95-9027-8_9

The Rise of Multimodal AI in Healthcare: A New Frontier

Venkatesh, U., Santhosh, V. N., & Mittal, M. (2026) · Springer eBooks · doi:10.1007/978-981-95-9027-8_1

Design and Evaluation of a Multi-Modal AI Diagnostic System with Real-Time Human-AI Feedback

Amirthamani, D. H. V., Hemavathy, P., Kalpana, S., Usha, P., GulMohamed, R. B., & Parimala, A. B. (2026) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict68280.2026.11511100

Mitochondrial dysfunction in Alzheimer’s disease: connecting pathophysiology with neuroimaging

Hu, J., Lai, J., Zhang, B., Jiang, X., Ma, H. F., & Liu, Y. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1748227

A comprehensive review of ischemic heart disease: pathophysiology, current treatments, natural products-based therapies, and nanotherapeutics

Saadeldeen, A. M., Mansour, A., El‐Dessouki, A. M., Fahim, S. A., Shaheen, A. M., Ismail, R. A., Salama, R. M., El‐Shiekh, R. A., & Khalifa, H. O. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1834343

Human Karyotype Analysis Using Deep Learning with Chatbot Assistance

Vandana, B. S., Hanai, A., Ashwini., B., Shedbalkar, D., Shetty, D., Dhyanchand, & Mathias, A. (2026) · International Conference on Artificial Intelligence and Data Engineering (AIDE) · doi:10.1109/aide69088.2026.11545006

A Pilot Study Protocol for AI-Assisted Interpretation of Chest X-rays for Pulmonary Abnormalities in Uganda

Obungoloch, J., Tumusiime, J., Nkwanga, J., Mugyenyi, G. R., Mbusa, C., Kaggwa, F., Celi, L. A., Haberer, J. E., & Wasswa, W. (2026) · Cureus · doi:10.7759/cureus.110504

Beyond Single Biomarkers: Multimodal Machine Learning for Precision Medicine Across Clinical Specialties

Magwenzi, S. (2026) · Journal of Precision Medicine and Artificial Intelligence · doi:10.64949/kap5fa07

Multimodal and Explainable Artificial Intelligence for Precision Healthcare: Integrating Federated Learning, Governance, and Affective Computing

Iglesias, V. S., Modi, N. J., Purackal, R. S., Shoukat, K., Koyun, A., Nangolo, M. N., & Park, K. (2026) · Westcliff International Journal of Applied Research · doi:10.47670/wuwijar20262vnrkamk

Diagnostic Performance of Contemporary Large Language Models on Free-Text Histopathologic Descriptions in Oral and Maxillofacial Pathology

Shujaat, S., Pillai, A. G., Riaz, M., Alharbi, W., Ganganna, K., Alfadley, A. A., Aboalela, A. A., & Abolemaaty, W. (2026) · Head and Neck Pathology · doi:10.1007/s12105-026-01929-9

Privacy-Preserving Multimodal Fusion with Dual Personalization for Medical Diagnostic Foundation Models

Chowdhury, S. H., Hossen, M. S., Miraz, M. M. I., Daiyan, A. H., Abdullah, M. I., Chowdhury, A. A., & Ifty, R. A. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11546332

Deep learning for intravascular optical coherence tomography: a review of plaque analysis

Chen, Y., To, M., Carpenter, H. J., Psaltis, P. J., & Li, J. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11607-5

Artificial intelligence for the integrative triangle: facial trauma, oral health, and systemic diseases

Pham, T. D. (2026) · Exploration of Medicine · doi:10.37349/emed.2026.1001413

Multimodal echocardiographic techniques in the diagnosis of cardiac tumors: applications and recent advances

Li, Y., & Liu, Y. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1750283

A Multimodal Deep Learning Approach for Cardiac Risk Assessment

Khelifi, A. M. S., Kahil, M. S., Derdour, M., Deriche, M., Elkabawi, G., & Nasor, M. (2026) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd69655.2026.11559040

A Multimodal Transformer Architecture for Early Detection of Diabetes Using Clinical, Wearable, and Demographic Data

Kavitha, K., Maheer, S., Basha, S. N., Sravani, M., Sravani, K., & Sreevidya, T. (2026) · International Conference on AI-Driven Innovations & Applications (IC-AIDA) · doi:10.1109/ic-aida68291.2026.11564489

A spatiotemporal state-inference framework for adaptive immunotherapy in glioblastoma

Chen, X., Li, S., Liu, X., & Ma, W. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1875352

Contactless Monitoring of Physiological Signals: Trends and Practical Applications

Hrubý, D., Barvík, D., & Černý, M. (2026) · International Conference on Microwaves, Radar & Wireless Communications · doi:10.23919/mikon66970.2026.11578001

Artificial intelligence in healthcare: historical development, terminology, concepts, and classifications

Иванович, К. Д., Lamotkin, A. I., & Анатольевич, Л. И. (2026) · FARMAKOEKONOMIKA Modern Pharmacoeconomics and Pharmacoepidemiology · doi:10.17749/2070-4909/farmakoekonomika.2026.372

Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020–2025): A Systematic Review

Hassan, W. A., Meligy, O. E., & Talaat, I. M. (2026) · Prostate Cancer · doi:10.1155/proc/9923959

Analytical methods in real-world data/evidence (RWD/E): a critical review

Kiryowa, I., Draguns, V., & Boulkrane, M. S. (2026) · Real-World Data & Evidence · doi:10.37489/2782-3784-myrwd-103

Multimodal AI fusion: integrating MRI with PET/CT, histopathology, and liquid biopsy for bone tumor diagnosis

Li, G., Lin, Y., & Wang, X. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1724332

A framework focused on deployment for pulmonary disease classification multimodal deep learning

Hamza, H. R. I. D., Machkour, M., & Asimi, Y. (2026) · EPJ Web of Conferences · doi:10.1051/epjconf/202638100021

Deep Learning for Early Disease Detection: A Multimodal Approach to Diagnostic Accuracy in Medical Imaging

VYAS, N. V., Velukuri, K., Alagingi, N. K., Kashyap, N., Balan, R., & Vishnuraj Nair, P. T. (2026) · IEEE Global Symposium on Emerging and Communication Technologies (GSEACT) · doi:10.1109/gseact68539.2026.11620446

Multimodal AI in Pulmonary Healthcare: Advancements, Challenges and an Innovative Approach for Early Detection

Hamid, E. A., Belalem, G., Mahmoudi, S., & Barigou, F. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-29325-1_2

Multimodal artificial intelligence in medical biotechnology: Integrating genomics, imaging, and clinical data for precision therapeutics

Dejen, G. M. (2026) · Precision Medical Sciences · doi:10.1002/prm2.70047

Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps

Fathima, J. H. S., Narendran, M. R., Quadri, M. S. A., Vinothkumar, T. S., Kamala, K., & Karobari, M. I. (2026) · Analytical Cellular Pathology · doi:10.1155/ancp/9281281

Advancing Preclinical Oncology: Next‐Generation Mouse Models for Decoding Tumor Progression and Metastasis

Sharma, A., Kumar, A., Patel, P., & Kurmi, B. D. (2026) · Advanced Therapeutics · doi:10.1002/adtp.70167

Proposed EthMed Platform: An AI-Enabled, Blockchain-Governed and Digital-Twin-Ready Health Platform for Ethnic Minority Communities in Remote and Hard-To-Reach Areas

Hung, L. N., Quang, V. Q., Tuan, N. T., Hieu, L. N., & Thuy, D. N. (2026) · doi:10.17352/hiim.000002

A Review of Deep Learning Models and Explainable Artificial Intelligence for Pathology Recognition

Toala-Paz, A., Becerra-Andrade, A., Cevallos-Levicek, D., Peláez, E., Domínguez, F., & Loayza, F. R. (2026) · International Conference on eDemocracy & eGovernment · doi:10.1109/icedeg70169.2026.11695396

Non-invasive preoperative MRI-based deep transfer learning radiomics model for predicting meningioma brain invasion

Yuan, D., Zhang, J., Zhang, C., Jing, X., Quan⁃zhi, F., & Han, T. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1935311

A Modular Platform for Multimodal Clinical Research: Architecture and Case Study on Glaucoma Classification

Lozano, A., Gonzalo‐Martín, C., & Garcia-Pedrero, A. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00135

Artificial Intelligence in Modern Healthcare: Present and Future

Koç, A. M. (2026) · Developments and Experiments in Health and Medicine · doi:10.18614/dehm.2003911

Künstliche Intelligenz in der Radiologie: Positionspapier der Österreichischen Röntgengesellschaft (ÖRG)

Kau, T., Furtner-Srajer, J., Adelsmayr, G., Widmann, G., Neumann, C., Perkonigg, M., Woitek, R., Lång, K., Rubeis, G., Pedevilla, M., et al. (2026) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2965-2624

Advances in Multimodal Artificial Intelligence in Radiology: Data Integration, Foundation Models, and Clinical Applications—A Narrative Review

Alfattni, G. (2026) · Healthcare · doi:10.3390/healthcare14193216

Multimodal artificial intelligence models for radiology

Tariq, A., Banerjee, I., Trivedi, H., & Gichoya, J. W. (2025) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubae017 · Cited by 13 · see citing works

Artificial Intelligence in Radiology: Methodological Foundations, Clinical Applications, and Emerging Multimodal Models

Stueckle, C. A., Limbrock, M., & Haage, P. (2026) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2944-0073

Ai-driven multi-modal radiology imaging for early detection of complex diseases: current advances, challenges, and future perspectives

Kataria, T. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21504943

Multimodal AI Imaging: The Evolving Landscape of Radiologist Efficiency and Decision Making

Karimkhani, A., Salhi, I., Bhakta, Y., Ellingham, E., Sandhu, A. S., & Zanetti, C. (2026) · RVU Elevate · doi:10.82451/k5963