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Digital pathology and multimodal learning on oncology data.

Asim Waqas1,2, Javeria Naveed3, Warda Shahnawaz4

  • 1Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institsute, Tampa, FL, 33612, United States.

BJR Artificial Intelligence
|May 1, 2026
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Summary

Integrating multimodal oncology data, including digital pathology (DP), enhances cancer diagnosis and treatment. Artificial intelligence and machine learning unlock new insights for personalized cancer care and improved patient outcomes.

Keywords:
artificial intelligencecancer researchcomputational pathologydigital pathologymachine learningmultimodal learningoncology data integration

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Area of Science:

  • Oncology
  • Digital Pathology
  • Artificial Intelligence in Medicine

Background:

  • Cancer is a complex disease with diverse biological, clinical, and molecular features.
  • Traditional oncology data analysis struggles with cancer's heterogeneity and multimodal nature.
  • Digital pathology (DP) is an emerging field for cancer analysis.

Purpose of the Study:

  • To review advancements in integrating multimodal oncology data within digital pathology.
  • To explore how DP leverages clinical, radiological, and molecular data.
  • To examine opportunities and challenges in multimodal learning for oncology.

Main Methods:

  • Review of recent literature on multimodal data integration in oncology and digital pathology.
  • Analysis of artificial intelligence, machine learning, and deep learning applications.
  • Examination of DP's role in combining diverse data modalities.

Main Results:

  • Multimodal learning, powered by AI, offers a more nuanced understanding of cancer.
  • Integrating diverse data types (clinical, radiological, molecular) with DP enhances analysis.
  • Synergistic potential exists for improving cancer diagnosis, treatment, and prognosis.

Conclusions:

  • Multimodal data integration is crucial for advancing digital pathology in oncology.
  • AI and machine learning are key enablers for multimodal learning in cancer care.
  • Continued innovation promises to transform cancer diagnosis, treatment planning, and surveillance.