Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Artificial intelligence in healthcare: past, present and future

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works

Artificial intelligence in healthcare

Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works

Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917 · see citing works

Artificial Intelligence and Deep Learning in Ophthalmology

Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A., Raman, R., Tan, G. S. W., Schmetterer, L. F., Keane, P. A., & Wong, T. Y. (2018) · Springer eBooks · doi:10.1007/978-3-030-64573-1_200 · Cited by 1,616 · see citing works

Artificial intelligence and deep learning in ophthalmology

Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2018-313173 · Cited by 1,554

Big data and machine learning algorithms for health-care delivery

Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · see citing works

Untitled work

(2015) · neural information processing systems · Cited by 912 · see citing works

Artificial intelligence for multimodal data integration in oncology

Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777 · see citing works

Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.004 · Cited by 660 · see citing works

Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1

Zoubin Ghahramani, A., Welling, M., Cortes, C., Lawrence, N. D., & Weinberger, K. Q. (2014) · Cited by 656 · see citing works

Artificial intelligence and computational pathology

Cui, M., & Zhang, D. Y. (2021) · Laboratory Investigation · doi:10.1038/s41374-020-00514-0 · Cited by 544 · see citing works

Artificial intelligence and machine learning for medical imaging: A technology review

Montero, A. M. B., Javaid, U., Valdés, G., Nguyen, D. T. D., Desbordes, P., Macq, B. M. M., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.04.016 · Cited by 481 · see citing works

Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review

Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P. D., Pisani, A. R., & Turner, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106649 · Cited by 407 · see citing works

Application of artificial intelligence in wearable devices: Opportunities and challenges

Nahavandi, D., Alizadehsani, R., Khosravi, A., & Acharya, U. R. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106541 · Cited by 392 · see citing works

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study

Lubitz, S. A., Faranesh, A. Z., Selvaggi, C. A., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., McConnell, M. V., Pantelopoulos, A., & Foulkes, A. S. (2022) · Circulation · doi:10.1161/circulationaha.122.060291 · Cited by 380 · see citing works

Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., et al. (2024) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.125 · Cited by 340 · see citing works

Ad Hoc Autonomous Agent Teams: Collaboration without Pre-Coordination

Stone, P., Kaminka, G., Kraus, S., & Rosenschein, J. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7529 · Cited by 323 · see citing works

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Vișan, A. I., & Neguț, I. (2024) · Life · doi:10.3390/life14020233 · Cited by 321 · see citing works

Artificial intelligence in medical imaging

Gore, J. C. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.12.006 · Cited by 277 · see citing works

An Analysis of Learners’ Intentions Toward Virtual Reality Learning Based on Constructivist and Technology Acceptance Approaches

Huang, H., & Liaw, S.-S. (2018) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v19i1.2503 · Cited by 240 · see citing works

Augmented Reality Technology Using Microsoft HoloLens in Anatomic Pathology

Hanna, M. G., Ahmed, I., Nine, J. S., Prajapati, S., & Pantanowitz, L. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2017-0189-oa · Cited by 213 · see citing works

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Gholap, A. D., Uddin, M. J., Faiyazuddin, M., Omri, A., Gowri, S., & Khalid, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108702 · Cited by 195 · see citing works

Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

Rajpara, S. M., Botello, A. P., Townend, J., & Ormerod, A. D. (2009) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2009.09093.x · Cited by 186 · see citing works

Digital image analysis outperforms manual biomarker assessment in breast cancer

Stålhammar, G., Martínez, N. F., Lippert, M. F., Tobin, N. P., Mølholm, I., Kis, L. L., Rosin, G., Rantalainen, M., Pedersen, L., Bergh, J. C. S., et al. (2016) · Modern Pathology · doi:10.1038/modpathol.2016.34 · Cited by 184 · see citing works

Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments

Wang, W., & Hsu, W.-S. (2023) · Sensors · doi:10.3390/s23135913 · Cited by 163 · see citing works

Detection of calibration drift in clinical prediction models to inform model updating

Davis, S. E., Greevy, R. A., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103611 · Cited by 160 · see citing works

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

Singh, S., Kumar, R., Payra, S., & Singh, S. K. (2023) · Cureus · doi:10.7759/cureus.44359 · Cited by 154 · see citing works

Artificial intelligence in metabolomics: a current review

Chi, J., Shu, J., Li, M., Mudappathi, R., Jin, Y., Lewis, F., Boon, A., Qin, X., Liu, L., & Gu, H. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117852 · Cited by 132 · see citing works

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Mutlag, A. A., Ghani, M. K. A., Mohammed, M. A., Maashi, M. S., Mohd, O., Mostafa, S. A., Abdulkareem, K. H., Marques, G., & Díez, I. D. L. T. (2020) · Sensors · doi:10.3390/s20071853 · Cited by 114 · see citing works

A nonparametric updating method to correct clinical prediction model drift

Davis, S. E., Greevy, R., Fonnesbeck, C., Lasko, T., Walsh, C. G., & Matheny, M. E. (2019) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocz127 · Cited by 112 · see citing works

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Brancato, V., Esposito, G., Coppola, L., Cavaliere, C., Mirabelli, P., Scapicchio, C., Borgheresi, R., Neri, E., Salvatore, M., & Aiello, M. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04891-8 · Cited by 106 · see citing works

Impact of virtual reality use on the teaching and learning of vectors

Campos, E., Hidrogo, I., & Zavala, G. (2022) · Frontiers in Education · doi:10.3389/feduc.2022.965640 · Cited by 99 · see citing works

A first-in-human phase 1a study of the bispecific anti-DLL4/anti-VEGF antibody navicixizumab (OMP-305B83) in patients with previously treated solid tumors

Jimeno, A., Moore, K. N., Gordon, M. S., Chugh, R. K., Diamond, J. R., Aljumaily, R. M., Mendelson, D. S., Kapoun, A. M., Xu, L., Stagg, R., et al. (2018) · Investigational New Drugs · doi:10.1007/s10637-018-0665-y · Cited by 82 · see citing works

Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial

Abramoff, M. D., Whitestone, N., Patnaik, J. L., Rich, E., Ahmed, M., Husain, L., Hassan, M. Y., Tanjil, M. S. H., Weitzman, D., Dai, T., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00931-7 · Cited by 81 · see citing works

Pathology Education Powered by Virtual and Digital Transformation: Now and the Future

Hassell, L. A., Absar, S. F., Chauhan, C., Dintzis, S. M., Farver, C. F., Fathima, S., Glassy, E. F., Goldstein, J. A., Gullapalli, R. R., Ho, J., et al. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2021-0473-ra · Cited by 74 · see citing works

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

Mikdadi, D., O’Connell, K. A., Meacham, P. J., Dugan, M. A., Ojiere, M. O., Carlson, T. B., & Klenk, J. A. (2022) · Cancer Biomarkers · doi:10.3233/cbm-210301 · Cited by 57 · see citing works

Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists

Hanna, M. G., Olson, N. H., Zarella, M. D., Dash, R. C., Herrmann, M. D., Furtado, L. V., Stram, M. N., Raciti, P. M., Hassell, L. A., Mays, A., et al. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0042-cp · Cited by 51 · see citing works

Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness

Ankolekar, A., Eppings, L., Bottari, F., Pinho, I. F., Howard, K., Baker, R., Yang, N., Xing, X., Walsh, S. L. F., Vos, W. G., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.014 · Cited by 48 · see citing works

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking

Frascarelli, C., Bonizzi, G., Musico, C. R., Mane, E., Cassi, C., Guerini‐Rocco, E., Farina, A., Scarpa, A., Lawlor, R. T., Bonetti, L. R., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13091390 · Cited by 47 · see citing works

Ki67 Quantitative Interpretation: Insights using Image Analysis

Volynskaya, Z. I., Mete, Ö., Pakbaz, S., AlGhamdi, D. A., & Asa, S. L. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_76_18 · Cited by 44 · see citing works

Application of automated image analysis reduces the workload of manual screening of sentinel lymph node biopsies in breast cancer

Holten‐Rossing, H., Talman, M., Jylling, A. M. B., Lænkholm, A., Kristensson, M., & Vainer, B. (2017) · Histopathology · doi:10.1111/his.13305 · Cited by 42 · see citing works

Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19

Hijazi, H., Talib, M. W. A., Hasasneh, A., Nassif, A. B., Ahmed, N., & Nasir, Q. (2021) · Sensors · doi:10.3390/s21248424 · Cited by 42 · see citing works

MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management

Huang, Z., & Tanaka, F. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263689 · Cited by 38 · see citing works

Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

Fu, X., Sahai, E. A., & Wilkins, A. C. (2023) · The Journal of Pathology · doi:10.1002/path.6153 · Cited by 36 · see citing works

Applications of Image Analysis to Anatomic Pathology: Realities and Promises

Gil, J., & Wu, H. (2003) · Cancer Investigation · doi:10.1081/cnv-120025097 · Cited by 34 · see citing works

Clinical Application of Image Analysis in Pathology

Cornish, T. C. (2020) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000263 · Cited by 34 · see citing works

Immunogradient Indicators for Antitumor Response Assessment by Automated Tumor-Stroma Interface Zone Detection

Rasmusson, A., Zilenaite-Petrulaitiene, D., Nestarenkaitė, A., Augulis, R., Laurinavičienė, A., Ostapenko, V., Poškus, T., & Laurinavičius, A. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.01.018 · Cited by 32 · see citing works

On Edge Computing for Remote Pathology Consultations and Computations

Sacco, A., Esposito, F., Marchetto, G., Kolar, G. R., & Schwetye, K. E. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3007661 · Cited by 31 · see citing works

Machine Learning Enhanced Virtual Autopsy

Sullivan, S. O., Holzinger, A., Zatloukal, K., Saldiva, P. H. N., Sajid, M. I., & Wichmann, D. (2017) · Autopsy and Case Reports · doi:10.4322/acr.2017.037 · Cited by 30 · see citing works

AI/ML advances in non-small cell lung cancer biomarker discovery

Çalışkan, M., & Tazaki, K. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1260374 · Cited by 27 · see citing works

Digital twin concept: Healthcare, education, research

Peshkova, M. A., Alekseevna, Y. V., Rudenko, E., Kretova, N. V., Timashev, P. S., & Демура, Т. А. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100313 · Cited by 26 · see citing works

Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection?

Mäkynen, M., Ng, G. A., Li, X., & Schlindwein, F. S. (2022) · Sensors · doi:10.3390/s22228588 · Cited by 23 · see citing works

Multi-agent medical image segmentation: A survey

Bennai, M. T., Guessoum, Z., Mazouzi, S., Cormier, S., & Mezghiche, M. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107444 · Cited by 23 · see citing works

Synthetic Data and Its Utility in Pathology and Laboratory Medicine

Pantanowitz, J., Manko, C. D., Pantanowitz, L., & Rashidi, H. H. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102095 · Cited by 20 · see citing works

Toward a standard formal semantic representation of the model card report

Amith, M. “., Cui, L., Zhi, D., Roberts, K. E., Jiang, X., Li, F., Yu, E., & Tao, C. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04797-6 · Cited by 19 · see citing works

Biobanking in the digital pathology era

Bonizzi, G., Zattoni, L., & Fusco, N. (2021) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.32604/or.2022.024892 · Cited by 19 · see citing works

Machine Learning in Multimodal Medical Imaging

Xia, Y., Ji, Z., Krylov, A. S., Chang, H., & Cai, W. (2017) · BioMed Research International · doi:10.1155/2017/1278329 · Cited by 16 · see citing works

Autonomous agents and multi-agent systems applied in healthcare

Montagna, S., Silva, D. C., Abreu, P. H., Ito, M., Schumacher, M., & Vargiu, E. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.02.007 · Cited by 15 · see citing works

Yes we care!-Certification for machine learning methods through the care label framework

Morik, K. J., Kotthaus, H., Fischer, R., Mücke, S., Jakobs, M., Piatkowski, N., Pauly, A., Heppe, L., & Heinrich, D. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.975029 · Cited by 15 · see citing works

Uncovering hidden and complex relations of pandemic dynamics using an AI driven system

Demirbaga, Ü., Kaur, N., & Aujla, G. S. (2024) · Scientific Reports · doi:10.1038/s41598-024-65845-0 · Cited by 10 · see citing works

Closing the AI Accountability Gap: Strict Liability and Punitive Damages for Advanced Artificial IntelligencePreprint

Weil, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4694006 · Cited by 8 · see citing works

Organizational and Technical Security Considerations for Laboratory Cloud Computing

Krumm, N. (2022) · The Journal of Applied Laboratory Medicine · doi:10.1093/jalm/jfac118 · Cited by 5 · see citing works

Commentary: Leveraging Edge Computing Technology for Digital Pathology

Yousif, M. Q., Balis, U. J., Parwani, A. V., & Pantanowitz, L. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_112_20 · Cited by 4 · see citing works

Enhancing reproducibility of machine learning‐based studies in clinical journals through model cards

Dan, J., & Seeuws, N. (2023) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.15785 · Cited by 4 · see citing works

Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research

Amith, M. “., Cui, L., Roberts, K. E., & Tao, C. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587601 · Cited by 3 · see citing works

研究领域执着的执着Preprint

Huyền, N. T. T., Ho, M., & Huyen, D. M. (2021) · doi:10.31219/osf.io/b7mqt

Correction: MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management

Huang, Z., & Tanaka, F. (2022) · PLOS ONE · doi:10.1371/journal.pone.0265924

Generative Artificial Intelligence in Healthcare: Applications, Implementation Challenges, and Future Directions

Rabbani, S. A., El‐Tanani, M., Sharma, S., Rabbani, S. S., El‐Tanani, Y., Kumar, R. R., & Saini, M. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5030037 · Cited by 102 · see citing works

Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology

Tiwari, A. K., Widodo, Krisnawati, D. I., Tzou, K.-Y., & Kuo, T. (2026) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03952-4 · Cited by 52 · see citing works

AI-driven bioactive peptide discovery of next-generation metabolic biotherapeutics

Hamadou, M., & Mune, M. A. M. (2025) · Applied Food Research · doi:10.1016/j.afres.2025.101291 · Cited by 47 · see citing works

Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey

Vavekanand, R., Kumar, T., Kumar, S., Kumar, G., & Laghari, A. A. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10560-4 · Cited by 29 · see citing works

Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions

Garg, P. K., Krishna, M., Kulkarni, P., Horne, D. A., Salgia, R., & Singhal, S. S. (2025) · Cancers · doi:10.3390/cancers17172799 · Cited by 28 · see citing works

Artificial intelligence agents in healthcare research: A scoping review

Njei, B., Al-Ajlouni, Y. A., Sidney Kanmounye, U., Boateng, S., Loic Nguefang, G., Njei, N., Hamouri, S., & Al-Ajlouni, A. F. (2026) · PLOS One · doi:10.1371/journal.pone.0342182 · Cited by 27 · see citing works

Artificial intelligence-driven intelligent nanocarriers for cancer theranostics: A paradigm shift with focus on brain tumors

Pourmadadi, M., Shabestari, S. M., Abdouss, H., Rahdar, A., Fathi‐karkan, S., & Pandey, S. (2025) · Seminars in Oncology · doi:10.1016/j.seminoncol.2025.152429 · Cited by 27 · see citing works

A Machine Learning Approach for Predicting Maternal Health Risks in Lower-Middle-Income Countries Using Sparse Data and Vital Signs

Malde, A. K., Prabhu, V. G., Banga, D., Hsieh, M. T., Renduchintala, C., & Pirrallo, R. G. (2025) · Future Internet · doi:10.3390/fi17050190 · Cited by 26 · see citing works

Review on Emerging Frontiers in Medical Sensing: Innovations Shaping the Future of Healthcare

Kirubakaran, D. (2025) · Biomedical Materials & Devices · doi:10.1007/s44174-025-00385-3 · Cited by 23 · see citing works

Machine learning in cancer prognostic and diagnostic biomarkers: A promising approach for early cancer detection

Vosoughi, P., Naghib, S. M., & Takdehghan, G. (2025) · Sensors and Actuators Reports · doi:10.1016/j.snr.2025.100385 · Cited by 19 · see citing works

The Role of Whole Slide Imaging in AI-Based Digital Pathology: Current Challenges and Future Directions—An Updated Literature Review

Omoush, S. A., Alzyoud, J. A. M., El-Omari, N. K. T., & Alzyoud, A. J. A. (2026) · Journal of Molecular Pathology · doi:10.3390/jmp7010002 · Cited by 19 · see citing works

Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery

Khorsandi, D., Farahani, A., Zarepour, A., Khosravi, A., Iravani, S., & Zarrabi, A. (2025) · RSC Advances · doi:10.1039/d5ra03747f · Cited by 18 · see citing works

Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions

Olawade, D. B., Kade, A., Egbon, E., Usman, S. O., Fapohunda, O., Ijiwade, J. O., & Ogbonna, C. E. (2025) · Molecular Genetics and Genomics · doi:10.1007/s00438-025-02314-x · Cited by 18 · see citing works

Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system

Angeloni, M., Rizzi, D., Schoen, S., Caputo, A., Merolla, F., Hartmann, A., Ferrazzi, F., & Fraggetta, F. (2025) · Genome Medicine · doi:10.1186/s13073-025-01484-y · Cited by 16 · see citing works

Advances in artificial intelligence-envisioned technologies for protein and nucleic acid research

Gholap, A. D., & Omri, A. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104362 · Cited by 15 · see citing works

Advancements in Machine Learning for Brain Tumor Classification and Diagnosis: A Comprehensive Review of Challenges and Future Directions

Ghorbian, M., Ghorbian, ‪., & Ghobaei‐Arani, M. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10340-6 · Cited by 15 · see citing works

The role of artificial intelligence-based foundation models and “copilots” in cancer pathology: potential and challenges

Cheng, H., & Wong, C. C. (2025) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-025-03592-4 · Cited by 14 · see citing works

Enhanced digital pathology image recognition via multi-attention mechanisms: the MACC-Net approach

Liu, F., Wang, Z., Li, B., Wang, D., Liu, M., Gou, F., & Wu, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-17369-4 · Cited by 12 · see citing works

A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research

Guedes, J., Woldmar, N., Szasz, A. M., Wieslander, E., Pawłowski, K., Horvatovich, P., Malm, J., Szadai, L., Németh, I. B., Marko-Varga, G., et al. (2025) · Journal of Proteomics · doi:10.1016/j.jprot.2025.105493 · Cited by 11 · see citing works

A systematic literature review of machine learning-based resource allocation techniques in cloud computing

Rathee, A., & Dalal, S. (2025) · Computing · doi:10.1007/s00607-025-01526-8 · Cited by 11 · see citing works

Enhancing Thyroid Disease Diagnosis With Machine Learning and Counterfactual Explainable AI

Alam, M. Z., Rahman, R., Sozib, H. M., Ahmed, H., Hossain, A., Sabeena, A. A., Tasnim, A. F., Ahmed, F., Sarkar, M. I., & Erdei, T. I. (2026) · IEEE Access · doi:10.1109/access.2026.3663497 · Cited by 11 · see citing works

Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects

Mahmud, M. I., & Banerjee, T. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag229 · Cited by 11 · see citing works

The Role of Machine Learning in Management of Operating Room: A Systematic Review

Merghani, A. M. A., Esmail, A., Osman, A. M. E. M., Mohamed, N. A. A., Shentour, S. M. M. A., & Merghani, S. M. A. (2025) · Cureus · doi:10.7759/cureus.79400 · Cited by 10 · see citing works

Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives

Ding, W., Zhang, J.-X., Jin, Z., Hua, H. J., Zu, Q., Yang, S., Wang, W., Liu, S., Zhou, H.-F., & Shi, H.-B. (2025) · Discover Oncology · doi:10.1007/s12672-025-03254-z · Cited by 10 · see citing works

AI‐Assisted Design and Evaluation of SLM‐Ti64 Implants for Enhanced Bone Regeneration

Zaheer, M. U., Razzaq, M. H., Aycan, M. F., & Mishra, Y. K. (2025) · Advanced Healthcare Materials · doi:10.1002/adhm.202503154 · Cited by 10 · see citing works

The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?

El‐Khoury, R., & Zaatari, G. S. (2025) · Diagnostics · doi:10.3390/diagnostics15182308 · Cited by 10 · see citing works

Potential biomarkers for early detection of endometriosis: current state of art (what we know so far)

Kliber, M., Kulczyńska-Figurny, K., Jagodziński, P. P., & Pławski, A. (2025) · Journal of Applied Genetics · doi:10.1007/s13353-025-01021-y · Cited by 10 · see citing works

A Unified CNN-Based Instance Segmentation Architecture for Blood Cell Classification and Early Cancer Abnormality Recognition

Dumayas, N. H., Merced, R. A. C., Rit, K. A., Verzosa, G. M. B., & Comia, L. V. (2026) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi67283.2026.11412802 · Cited by 10 · see citing works

Community engagement, recruitment, and retention of minoritized participants in Alzheimer's disease and related dementia research: A systematic review of disparities

Assfaw, A. D., Babulal, G. M., Balls‐Berry, J. E., Mozersky, J. T., Moulder, K. L., & Morris, J. C. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70459 · Cited by 9 · see citing works

Design specifications for biomedical virtual twins in engineered adoptive cellular immunotherapies

Weirauch, U., Kreuz, M., Birkenbihl, C., Alb, M., Quaranta, M., Calzone, L., Orozco-Ruiz, S., Binder, S., Fischer, L., Clavreul, S., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01809-6 · Cited by 9 · see citing works

Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers

Yap, M., Mihai, I. M., & Wang, G. (2026) · Current Oncology · doi:10.3390/curroncol33010031 · Cited by 9 · see citing works

AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery

Chisompola, D., Luwaya, E. L., Nzobokela, J., Mwansa, P., & Chakulya, M. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1717859 · Cited by 9 · see citing works

Position of artificial intelligence in healthcare and future perspective

Çiçek, V., & Bağcı, U. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103193 · Cited by 8 · see citing works

GPU-Accelerated Artificial Intelligence Applications in Cancer Diagnosis, Imaging, and Treatment Planning

Montazer, F., Mehdikhani, B., Noroozi, N., Nezameslami, R., Nezameslami, A., Shahbazi, A., Jayervand, F., Naseri, A., Rahmani, A., Negahi, A., et al. (2025) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2025.26.8.2725 · Cited by 8 · see citing works

Artificial Intelligence and Digital Tools Across the Hepato-Pancreato-Biliary Surgical Pathway: A Systematic Review

Efstathiou, A., Charitaki, E., Triantopoulou, C., & Delis, S. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14186501 · Cited by 8 · see citing works

Determinants of Trust in Artificial Intelligence (AI) for Health-Related Decision-Making Among Adults in Saudi Arabia: A Cross-Sectional Study

Alharbi, B. S., Aljabri, M. M., & Ali, E. A. (2026) · Healthcare · doi:10.3390/healthcare14040506 · Cited by 7 · see citing works

Agentic artificial intelligence-driven tutoring: A multi-agent cognitive architecture for personalized adaptive learning in Education

Gupta, N., & Heggond, S. B. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0202022 · Cited by 7 · see citing works

Responsible agentic artificial intelligence governance: Risk, safety, and ethical challenges in autonomous systems

BISWAS, B., & Sarkar, S. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0202005 · Cited by 7 · see citing works

Advancing Clinical Decision-Making using Artificial Intelligence and Machine Learning for Accurate Disease Diagnosis

Gupta, R., Kakani, T. A., Vedula, J., Mohammed, M., Hudani, K., & Yuvaraj, N. (2025) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv64824.2025.11085548 · Cited by 7 · see citing works

Integrating Machine Learning and Fractional-Order Dynamics for Enhanced Psoriasis Prediction and Clinical Decision Support

Amilo, D., Sadri, K., Hınçal, E., & Hafez, M. (2025) · AppliedMath · doi:10.3390/appliedmath5040143 · Cited by 6 · see citing works

A study on promoting AI learning and usage behaviors among health management students from the perspective of the “knowledge-belief-action” model

Du, J., Zhu, L., Min, X., Chang, X., Qi, W., Wei, T., Wei, S., Zhang, X., Huang, J., Tao, X., et al. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1747362 · Cited by 6 · see citing works

Comparison Feed Forward Back Propagation Networks (FFBPNs) with Support Vector Machine (SVM) for Diagnosis Skin Cancer Based on Images

Jawad, R., & Jawad, R. (2025) · Vokasi UNESA Bulletin of Engineering, Technology and Applied Science · doi:10.26740/vubeta.v2i2.36117 · Cited by 6 · see citing works

Virtual reality and mixed reality in the assessment of spatial memory

García-Navarra, S., Solares, L., & Méndez, M. (2025) · Exploration of Digital Health Technologies · doi:10.37349/edht.2025.101151 · Cited by 5 · see citing works

Internet of Medical Things Enabled Multimodal Framework: Deep Machine Learning for Chronic Cardiac Disease Prediction in Healthcare 5.0

Javed, R., Abbas, T., Sayyed, A., Abbas, S., Shah, A. A., & Khan, M. A. (2026) · Healthcare Technology Letters · doi:10.1049/htl2.70063 · Cited by 5 · see citing works

Agentic artificial intelligence in education: A review on personalized adaptive learning through autonomous tutoring systems

Meti, A. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0202002 · Cited by 5 · see citing works

Navigation of drug discovery via artificial intelligence

Mishra, S. K., Praba, J. J., Hamadou, M., Subba, A., & Georrge, J. J. (2026) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-026-00954-3 · Cited by 5 · see citing works

Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis

Faa, G., Lai, E., Cau, F., Coghe, F., Rugge, M., Suri, J. S., Codipietro, C., Congiu, B., Graziano, S., Tiwari, E., et al. (2026) · Cancers · doi:10.3390/cancers18071184 · Cited by 5 · see citing works

The Use of Blockchain Technology and Artificial Intelligence in Cryptocurrency and Medical Technology

Gwala, R. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8664-4.ch007 · Cited by 5 · see citing works

Digital pathology and AI in forensic medicine: advances, limitations, and medico-legal utility

Singh, J. P., Tyagi, S., Sharma, A., Chanana, A., & Basra, S. (2025) · Academia Medicine · doi:10.20935/acadmed7799 · Cited by 5 · see citing works

Point-of-Care Testing and Biomarkers in Biliary Diseases: Current Evidence and Future Directions

Jung, J. H., Lee, K. J., Park, S. W., Koh, D. H., & Lee, J. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14196724 · Cited by 5 · see citing works

Patient safety in AI-powered diagnostic pathology

Rugge, M., Fraschini, M., Orvieto, E., Didaci, L., Sandonà, L., D’Amuri, A., Saba, L., & Faa, G. (2025) · Journal of Clinical Pathology · doi:10.1136/jcp-2025-210231 · Cited by 5 · see citing works

Breaking the Silence: AI-Enhanced Multi-Omics biosensors for liquid Biopsy-Based early diagnosis of pancreatic cancer

Aydemir, M., Kömür, A. İ., Gündüz, E. S., Karaman, C., & Karaman, O. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.114118 · Cited by 4 · see citing works

Artificial Intelligence and Machine Learning in Cancer Drug Discovery, Nanomedicine, and Precision Oncology

Habeeb, M., Vengateswaran, H. T., Suseela, P., Grace, X., & Rani, T. S. (2025) · doi:10.70593/978-93-7185-515-0 · Cited by 4 · see citing works

Attitudes toward artificial intelligence in pathology: a survey-based study of pathologists in northern India

Sharma, M., Kumari, K., Navpreet, N., Bharti, S., & Kumari, R. (2025) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2025.07.10 · Cited by 4 · see citing works

Cervical cancer diagnostics: non-coding RNAs and biosensors to AI-derived methods

Mousavinejad, S. N., Lachouri, R., Ferdosi, F., & Khatami, S. H. (2025) · Clinica Chimica Acta · doi:10.1016/j.cca.2025.120641 · Cited by 4 · see citing works

A multiclass machine learning framework for chronic kidney disease staging using CTGAN-based synthetic data augmentation and explainable AI

Gogoi, P., & Valan, J. A. (2026) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2025.2610677 · Cited by 4 · see citing works

Thermal Stress Analysis via Infrared Imaging for Safety Monitoring in Biomedical Electronic Devices

Laganà, F., Pratticò, D., Calcagno, S., Carlo, D. D., Foresta, F. L., & Angiulli, G. (2025) · International Workshop on Biomedical Applications, Technologies and Sensors (BATS) · doi:10.1109/bats67559.2025.11336186 · Cited by 4 · see citing works

Estrogen Receptor-Low Positive (ER-Low) Breast Cancer: A Unique Clinical and Pathological Entity

Faa, G., Lai, E., Ziranu, P., Pretta, A., Tiwari, E., Dessì, M., Solinas, C., Saba, G., Loi, F., Codipietro, C., et al. (2026) · Current Oncology · doi:10.3390/curroncol33020122 · Cited by 4 · see citing works

Biobanking in the Era of Artificial Intelligence: Convergence, Challenges, and Opportunities

Tran, N. K., Dhir, R., & Rashidi, H. (2026) · Biopreservation and Biobanking · doi:10.1177/19475535261445907 · Cited by 4 · see citing works

Artificial Intelligence in the Metaverse: Transforming Health in Smart Cities

Naeem, K., Daud, A. Y., Alsahfi, T., Badshah, A., & Cheema, S. (2025) · International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC) · doi:10.1109/icetecc65365.2025.11070256 · Cited by 3 · see citing works

The Ongoing Evolution of AI in Craniofacial Surgery: From Theory to Reality and Beyond

Pourriyahi, H., & Alkureishi, L. W. T. (2025) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000012123 · Cited by 3 · see citing works

Industry 6.0 and the Digital Transformation of Healthcare and Pharmaceuticals

Majeed, H., & Iftikhar, T. (2026) · Springer eBooks · doi:10.1007/978-3-032-07278-8_12 · Cited by 3 · see citing works

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Lahari, P. V., Dutta, S., Deeksha, H., Patel, S. A., Dehury, B., & Mazumder, N. (2026) · Microscopy Research and Technique · doi:10.1002/jemt.70112 · Cited by 3 · see citing works

Homomorphic encryption for secure and scalable predictive healthcare analytics: a review and case study

Gogoi, P., & Valan, J. A. (2026) · Life Cycle Reliability and Safety Engineering · doi:10.1007/s41872-025-00389-4 · Cited by 3 · see citing works

Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis

Hajikarimloo, B., Mohammadzadeh, I., Tos, S. M., Hashemi, R., Khoshrou, A., Amjadzadeh, M., Dehghan, M., Aghajan, S., Goudarzi, E., Najari, D., et al. (2026) · Neurocritical Care · doi:10.1007/s12028-026-02450-1 · Cited by 3 · see citing works

A Systematic Literature Review on Integrated Deep Learning and Multiagent Vision-Language Frameworks for Pathology Image Analysis and Report Generation

Ali, U., Shafi, I., Ahmad, J., Caceres, A. Z., Montero, T. C. C., Rehman, H. M. R. U., & Ashraf, I. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0023 · Cited by 3 · see citing works

Hybrid physics-informed machine learning and nanobiosensing strategies for precision liver cancer diagnostics

Rahdar, A., Shabestari, S. M., Najafi, M., Shirzad, M., & Pandey, S. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109025 · Cited by 3 · see citing works

Factors influencing teachers’ adoption of artificial intelligence tools in K-12 education

Kalawati, Sahu, S. K., & Swamy, N. K. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0202039 · Cited by 3 · see citing works

Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology

Tavares, T., Sangalli, L., Menon, R. S., Miller, C. S., & Hochman, G. (2026) · Head and Neck Pathology · doi:10.1007/s12105-026-01909-z · Cited by 3 · see citing works

Toward Precision Cardiac Rehabilitation: Current Limitations and Future Opportunities of Omics and Artificial Intelligence

Ghram, A., Diaz, J. L., Raghavan, P., Hautala, A. J., Kaddoura, R., Laukkanen, J. A., Kunutsor, S. K., Elrayess, M. A., Knechtle, B., Weiss, K., et al. (2026) · Sports Medicine · doi:10.1007/s40279-026-02453-0 · Cited by 3 · see citing works

Proteomic Profiling of Inflammatory Protein Dysregulation in HLA-B27-Positive Ankylosing Spondylitis: Molecular Signatures and Potential Biomarkers

Yan, Y., Wang, J., Wang, Y., Liu, J. T., Yang, W., Niu, M., Yu, Y., & Zhao, H. (2025) · Biomolecules · doi:10.3390/biom15040516 · Cited by 3 · see citing works

Case Report: The intersection of psychiatry and medicine: diagnostic and ethical insights from case studies

Monaco, F., Vignapiano, A., D’Angelo, M., Raffone, F., Stefano, V. D., Boccia, A., Longobardi, A., Gruttola, B. D., Fornaro, M., Corrivetti, G., et al. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1576179 · Cited by 3 · see citing works

Bibliometrics Analysis and Knowledge Mapping of Fragment-Based Drug Design Research: Trends from 2015 to 2024

He, C., Zhang, J., Zhang, R., Liu, L., Luo, J., Zhang, W., & Fang, B. (2025) · Drug Design Development and Therapy · doi:10.2147/dddt.s518489 · Cited by 3 · see citing works

Leveraging AI to Enhance Healthcare Delivery in Tanzania: Innovations and Ethical Imperatives

Mwogosi, A. (2025) · SAGE Open · doi:10.1177/21582440251378162 · Cited by 3 · see citing works

Artificial intelligence for arterial blood gas interpretation

Mousavinejad, S. N., Lachouri, R., Bahadorzadeh, M., & Khatami, S. H. (2025) · Clinica Chimica Acta · doi:10.1016/j.cca.2025.120691 · Cited by 3 · see citing works

Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps

Aydin, B., Koçak, Ö. F., Şebin, S. Ö., & Özgeriş, F. B. (2025) · Biological Trace Element Research · doi:10.1007/s12011-025-04718-7 · Cited by 2 · see citing works

Assessing the Clinical Utility of Multimodal Large Language Models in the Diagnosis and Management of Pigmented Choroidal Lesions

Mehta, N. N., Walker, E., Flester, E., Folk, G. A., Agnihotri, A. P., Nagel, I. D., Tran, M. D., Goldbaum, M. H., Borooah, S., & Scott, N. L. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.10.13 · Cited by 2 · see citing works

Biochemical, Biophysical, and Digital Biomarkers in Precision Healthcare

Sharma, R., Ghosh, G., Abbas, A. K., & Mahato, K. (2025) · Springer eBooks · doi:10.1007/978-981-95-2117-3_4 · Cited by 2 · see citing works

Food-derived linear vs. rationally designed cyclic peptides as potent TNF-alpha inhibitors: an integrative computational study

Shah, M., & Arumugam, S. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1716375 · Cited by 2 · see citing works

Artificial Intelligence and Machine Learning in Health Care

Liebovitz, D., Wu, A. Y., Reamer, C. B., & Vadasz, B. (2025) · Medical Clinics of North America · doi:10.1016/j.mcna.2025.07.011 · Cited by 2 · see citing works

Toward sustainable and scalable synthesis of ibuprofen: Integrative insights into batch and continuous flow strategies

Yang, W., Liu, Y., Li, R., Lv, J., Zhang, Y., Ren, Y., Yuan, Z., & Zhang, Z. (2025) · AIChE Journal · doi:10.1002/aic.70198 · Cited by 2 · see citing works

Generative artificial intelligence-driven adaptive learning for sustainable, personalized, and resilient education systems

Munenakoppa, M., Rane, N. L., Rane, J., & Heggond, S. B. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0202011 · Cited by 2 · see citing works

Artificial intelligence-driven green education for sustainable development goals: A review

Chaudhary, A. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0202001 · Cited by 2 · see citing works

Artificial intelligence in public health: a foundational shift or a technocratic distraction?

Sreya, B. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1789522 · Cited by 2 · see citing works

SAMACO_FS: feature selection for high-dimensional few instances using ant colony optimization algorithm and self-attention mechanism

Ye, Z., Song, A., Jin, H., Zhou, W., Cai, T., Wang, M., Mei, M., He, Q., & Cheng, X. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08409-5 · Cited by 2 · see citing works

Training the next-generation of biomedical scientists through artificial intelligence-driven education and research in pharmacology and pharmaceutical sciences

Kumar, S., Karwasra, R., Zhou, W., Seetharaman, J., & Singla, B. (2026) · Experimental Biology and Medicine · doi:10.3389/ebm.2026.10988 · Cited by 2 · see citing works

A belief rule-based two-sided medical decision model considering patient preferences and historical reasoning rules

Fang, R., Liao, H., Liu, X., & Jiang, L. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115229 · Cited by 2 · see citing works

Bridging diagnosis and therapeutics: The role of AI in cancer detection and drug development

Barua, S., Balaji, B., & Balaji, S. (2026) · Health Sciences Review · doi:10.1016/j.hsr.2026.100270 · Cited by 2 · see citing works

Applications of Artificial Intelligence in Urodynamic Data Interpretation: A Narrative Review

Pourghazi, F., Chaudhary, P. K., Linder, B. J., Alizad, A., & Fatemi, M. (2026) · Neurourology and Urodynamics · doi:10.1002/nau.70308 · Cited by 2 · see citing works

AI-Enhanced Diagnosis for Immunological Disorders

Neguț, I., & Vișan, A. I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch003 · Cited by 2 · see citing works

Introducing an Essential 7-Part Artificial Intelligence Review Series: A Guided Journey Into the Future of Pathology and Medicine

Rashidi, H. H., Hanna, M. G., & Pantanowitz, L. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100673 · Cited by 2 · see citing works

Application Of Electrical Impedance Tomography For Detecting Meat (Body Tissue): A Study On Frequency And Amplitude Variations

Aisya, R., Samatha, S. C., Ain, K., & Astuti, S. D. (2025) · Indonesian Journal of Electronics Electromedical Engineering and Medical Informatics · doi:10.35882/ijeeemi.v7i2.54 · Cited by 1 · see citing works

Personalized Human–Computer Collaboration Decision Making for Enhancing Breast Cancer Diagnosis

Fu, C., Cui, K., & Xue, M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2534071 · Cited by 1 · see citing works

Agent AI Becomes Next Addictive Disorder

Chen, A. P. S. (2025) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1109/iccai66501.2025.00044 · Cited by 1 · see citing works

Assessing the Environmental Impact of AI-Based Medical Diagnosis Using Grey Relational Analysis

Quttainah, M. A., Singh, P., & Rana, S. (2025) · المجلة العربية للعلوم الإدارية. · doi:10.34120/ajas.2025.1387 · Cited by 1 · see citing works

Artificial Intelligence and Machine Learning Approaches in Radiology for Medical Imaging Diagnostics

Gomase, V. S., & Ghatule, A. P. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965407792250811093550 · Cited by 1 · see citing works

Fundamentals of Machine Learning and Deep Learning for Healthcare Applications

Katta, S., Singh, P., Garg, D., & Diwakar, M. (2025) · Springer series in reliability engineering · doi:10.1007/978-3-031-98728-1_13 · Cited by 1 · see citing works

Super resolution of pathology images with hierarchical feature integration and local image patterns

Xu, F., Li, L., Wang, S., Ling, R., Zhang, X., Deng, X., Zhou, M., Ling, J., & Gao, C. (2025) · The Journal of Pathology · doi:10.1002/path.6482 · Cited by 1 · see citing works

Acute (destructive) pancreatitis: prediction of outcomes using artificial intelligence, a comprehensive review

Osmonaliev, K., Rehman, S. U., Kudayarov, E. E., Aknazarov, K., Sydygaliev, K., & Бекболсунович, А. С. (2025) · Brazilian Journal of Biology · doi:10.1590/1519-6984.294105 · Cited by 1 · see citing works

Advances in bioanalytical techniques for toxicological applications: Emerging techniques, challenges, and future directions

Singh, S., & Parmar, I. (2025) · Food and Chemical Toxicology · doi:10.1016/j.fct.2025.115842 · Cited by 1 · see citing works

Advancements and Future Directions of AI and Machine Learning in Pathology and Medicine

Shubhangi R Patil, A. P. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.16566583

Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative Artificial Intelligence Basics

Rashidi, H. H., Pantanowitz, J., Hanna, M. G., Tafti, A. P., Sanghani, P., Buchinsky, A., Fennell, B. D., Deebajah, M., Wheeler, S., Pearce, T. M., et al. (2025) · Modern Pathology · doi:10.1016/j.modpat.2024.100688 · Cited by 72 · see citing works

Preface: Artificial intelligence (AI), machine learning ML) and digital pathology integration are the next major chapter in our diagnostic pathology and laboratory medicine arena

Rashidi, H. H., & Chen, M. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.005 · Cited by 7 · see citing works

The Impact of Artificial Intelligence and Machine Learning in Medical Imaging

Amrita, A., Baniya, P., Agrawal, A., & Gupta, B. B. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-5261-8.ch014 · Cited by 1 · see citing works

Artificial intelligence in pathology: from image analysis to clinical decision support

Pytel, M., Fiegler-Rudol, J., Kronenberg, M., Cogiel, K., Osikowicz, M. D., & Męcik-Kronenberg, T. (2025) · Medical Studies · doi:10.5114/ms.2025.154278