The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine
Open accessAbstract
Artificial intelligence (AI) and machine learning (ML) are transforming the field of medicine. Health care organizations are now starting to establish management strategies for integrating such platforms (AI-ML toolsets) that leverage the computational power of advanced algorithms to analyze data and to provide better insights that ultimately translate to enhanced clinical decision-making and improved patient outcomes. Emerging AI-ML platforms and trends in pathology and medicine are reshaping the field by offering innovative solutions to enhance diagnostic accuracy, operational workflows, clinical decision support, and clinical outcomes. These tools are also increasingly valuable in pathology research in which they contribute to automated image analysis, biomarker discovery, drug development, clinical trials, and productive analytics. Other related trends include the adoption of ML operations for managing models in clinical settings, the application of multimodal and multiagent AI to utilize diverse data sources, expedited translational research, and virtualized education for training and simulation. As the final chapter of our AI educational series, this review article delves into the current adoption, future directions, and transformative potential of AI-ML platforms in pathology and medicine, discussing their applications, benefits, challenges, and future perspectives.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
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
Self-citationLarge-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
(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
Flagged first, then by citations
Research works printed but not linked: 2
- Weil, 2024, Tort law as a tool for mitigating catastrophic risk from artificial Intelligence, SSRN Journal
- Allain, 2013, From jeopardy! to jaundice: the medical liability implications of Dr. Watson and other artificial intelligence systems, LA Law Rev, 73, 4
Cited by
Sorted by citations
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
Garg, P. K., Krishna, M., Kulkarni, P., Horne, D. A., Salgia, R., & Singhal, S. S. (2025) · Cancers · doi:10.3390/cancers17172799 · Cited by 29
Metadata
- Authors
- Hanna, M. G., Pantanowitz, L., Dash, R., Harrison, J. H., Deebajah, M., Pantanowitz, J., & Rashidi, H. H.
- Affiliations
- University of Pittsburgh (US)
- Duke University (US)
- University of Virginia (US)
- Cleveland Clinic (US)
- Venue
- Modern Pathology
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 38
- Issue
- 4
- Pages
- 100705
- ISSN
- 0893-3952 (ISSN-L)
- OpenAlex
- W4406062782
- Semantic Scholar
- 92b21a25404ffeff52870cc13e590a1411404380
- Cited by
- 231
- References
- 68
- Citations by year
- 2026: 137 · 2025: 93
- FWCI
- 11.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Modern Pathology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, machine learning, pathology, clinical decision support, automated image analysis, biomarker discovery, drug development, clinical trials, MLOps, multimodal AI, translational research, online learning, diagnostic accuracy
- MeSH terms
- Machine Learning, Artificial Intelligence, Humans, Pathology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
