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

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

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

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

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

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 1,660

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

Untitled work

(2015) · neural information processing systems · Cited by 912

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

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

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

Artificial intelligence and computational pathology

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

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 510

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 411

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

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 397

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

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 333

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

Artificial intelligence in medical imaging

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

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

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

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 197

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

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 185

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

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

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

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 139

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

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

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

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

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 84

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

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

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 59

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

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 52

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 48

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

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

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 44

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

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 37

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

Clinical Application of Image Analysis in Pathology

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

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

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

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

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 28

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

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

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

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 22

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 20

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

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

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

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

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 12

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

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

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

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

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

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 119

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 55

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

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 30

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 29

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

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

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

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