Harnessing multimodal data integration to advance precision oncology
Open accessAbstract
Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.
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If underlying data contains complementary rather than mutual information, explanations should improve.
“If underlying data contains complementary rather than mutual information, explanations should improve7.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [7]
The Introduction says complementary, rather than fully mutual, information can improve multimodal inference. It does not say explanations improve; the section on interpretability instead describes explaining multimodal models as a separate challenge. No page numbers were available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSignificantly high costs and extensive data collection requirements for LLM training present major challenges in establishing foundation models for biomedical learning, particularly in collecting paired multimodal datasets from individual patients.
“The significantly high costs and extensive data collection requirements for LLM training present major challenges in establishing foundation models for biomedical learning, particularly in collecting paired multimodal datasets from individual patients [199].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [199]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredAI-augmented pipelines have the potential to reshape how we diagnose and treat cancer, ushering in a more data-driven era of precision oncology.
“AI-augmented pipelines have the potential to reshape how we diagnose and treat cancer, ushering in a more data-driven era of pre cision oncology [52].”
From A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research (2025) · cites this paper as [52]
Obtaining sufficient paired multimodal data in healthcare is challenging due to linking distinct modality-specific resources to the same patient or clinical encounter and inherent data fragmentation in healthcare systems.
“In healthcare, obtaining sufficient paired data is challenging due to the complexities associated with linking distinct modality-specific resources to the same patient or clinical encounter26, coupled with inherent data fragmentation in healthcare systems27.”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [26]
In oncology and cancer biomarker discovery, integrating omics within multimodal AI frameworks has demonstrated improved performance.
“In oncology [27] and cancer biomarker discovery [26], integrating omics within multimodal AI frameworks has demonstrated improved performance”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [26]
In cancer, overcoming challenges related to data access, sharing and accurate labeling could potentially lead to impactful tools that leverage combination of personalized omics data with histopathology, imaging and clinical data to inform clinical trajectories and improve patient outcomes.
“In the context of cancer, overcoming challenges related to data access, sharing and accurate labeling could potentially lead to impactful tools that leverage the combination of personalized omics data with histopathology, imaging and clinical data to inform clinical trajectories and improve patient outcomes 23 .”
From Multimodal biomedical AI (2022) · cites this paper as [23]
No review has systematically summarized the methodologies and processes specific to the application of multimodal AI in critical care.
“However, to the best of our knowledge, no review has systematically summarized the methodologies and processes specific to the application of multimodal AI in critical care.”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [26]
The Introduction (page unavailable) describes a review of multimodal data integration in cancer research, not critical care. Searches for critical care and for a systematic review of multimodal AI methodologies found no passage addressing whether such a review exists. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe vibe coding workflow helps clinician-researchers prototype quickly.
“This “vibe coding” workflow—iteratively stating the intent of the analysis and having the system draft or revise code—helps clinician-researchers prototype quickly [68].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [68]
Adaptability of LLMs across data formats is crucial in precision oncology, where data from multiple data formats must be integrated and analyzed to generate personalized patient profiles and treatment strategies.
“This adaptability is crucial in precision oncology, where data from multiple data formats—such as imaging reports, next-generation sequencing results, and laboratory results—must be integrated and analyzed to generate personalized patient profiles and treatment strategies [39].”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [39]
Different data modalities likely contain distinct and complementary information, making multimodal AI an active area of oncology research.
“These different data modalities are likely to contain distinct and complementary information; thus, multimodal AI is an active area of research in oncology (168, 169).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [168]
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“In healthcare, obtaining suf fi cient paired data is challenging due to the complexities associated with linking distinct modality-speci fi c resources to the same patient or clinical encounter 26 , coupled with inherent data fragmentation in healthcare systems 27 .”
Self-citationMultimodal Artificial Intelligence for Precision Critical Care: A Scoping Review
Yang, M., Shi, N., Chen, H., Po, E. Y. P., Ng, P. Y., Lai, P. C. K., Wu, Y.-C., & Fong, D. Y. T. (2026) · Health Data Science · doi:10.34133/hds.0356 · Cited by 3 · see citing works
“In oncology [ 27 ] and cancer biomarker discovery [ 26 ], integrating omics within multimodal AI frameworks has demonstrated improved performance, suggesting that omics data hold promise for exploration in future multimodal AI applications in critical care.”
- Authors
- Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Shah, Sohrab P.
- Venue
- Nature Reviews Cancer
- Published
- 2021-10-18
- Volume
- 22
- Issue
- 2
- Pages
- 114-126
- ISSN
- 1474-175X (ISSN-L)
- 1474-1768
- PMC
- PMC8810682
- OpenAlex
- W3205594709
- Cited by
- 662
- References
- 173
- Citations by year
- 2026: 183 · 2025: 195 · 2024: 134 · 2023: 104 · 2022: 44 · 2021: 2
- FWCI
- 11.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature reviews. Cancer (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (F30CA257414, P30CA008748)
- National Institute of General Medical Sciences (T32GM007739)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- multimodal data integration, precision oncology, multimodal biomarkers, multimodal learning, radiological imaging, histological imaging, molecular diagnosis, clinical data, data sparsity, cancer prognosis, treatment management
- MeSH terms
- Artificial Intelligence, Humans, Medical Oncology, Neoplasms, Genomics, Precision Medicine
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