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Cancer Res., Jul 02 2025, 85 (13) | https://doi.org/10.1158/0008-5472.CAN-24-3630
Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality
Pegah Khosravi 1,2, Thomas J Fuchs 3, David Joon Ho 4*
1Department of Biological Sciences, New York City College of Technology, City University of New York, Brooklyn, New York.
2Biology and Computer PhD Programs, The CUNY Graduate Center, City University of New York, New York, New York.
3Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York.
4Department of Public Health and AI, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea.
*Corresponding Author: David Joon Ho
1Department of Biological Sciences, New York City College of Technology, City University of New York, Brooklyn, New York.
2Biology and Computer PhD Programs, The CUNY Graduate Center, City University of New York, New York, New York.
3Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York.
4Department of Public Health and AI, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea.
*Corresponding Author: David Joon Ho
Abstract
The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offering unprecedented capabilities in image analysis, diagnosis, and treatment planning. AI techniques provide standardized assistance to clinicians, in which many diagnostic and predictive tasks are manually conducted, causing low reproducibility. These AI methods can additionally provide explainability to help clinicians make the best decisions for patient care. This review explores state-of-the-art AI methods, focusing on their application in image classification, image segmentation, multiple instance learning, generative models, and self-supervised learning. In radiology, AI enhances tumor detection, diagnosis, and treatment planning through advanced imaging modalities and real-time applications. In pathology, AI-driven image analysis improves cancer detection, biomarker discovery, and diagnostic consistency. Multimodal AI approaches can integrate data from radiology, pathology, and genomics to provide comprehensive diagnostic insights. Emerging trends, challenges, and future directions in AI-driven cancer research are discussed, emphasizing the transformative potential of these technologies in improving patient outcomes and advancing cancer care. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
논문정보
- Review Paper
- 2025년 07월 (BRIC 등록일 2025-07-04)
- 국내(교신)+국외 연구진
- 바이오·의료융합 > 다학제 융합/복합
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