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

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Hanna, Matthew G., Pantanowitz, Liron, Dash, Rajesh, Harrison, James H., Deebajah, Mustafa, Pantanowitz, Joshua, Rashidi, Hooman H.·2025·Modern Pathology

DOI 10.1016/j.modpat.2025.100705

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Abstract

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.

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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)
ORCID: Hanna, Matthew G.
Pantanowitz, Liron
Pantanowitz, Joshua
Rashidi, Hooman H.
Venue: Modern Pathology
Publisher: Elsevier BV
Published: 2025-04
Volume: 38
Issue: 4
Pages: 100705
ISSN: 0893-3952 (ISSN-L)
DOI: 10.1016/j.modpat.2025.100705
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
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

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

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