Artificial intelligence for multimodal data integration in oncology
Open accessAbstract
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.
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AI-driven medical imaging analysis significantly improves the accuracy and efficiency of diagnostic processes.
“AI-driven medical imaging analysis has emerged as a pivotal tool in modern healthcare, significantly improving the accuracy and efficiency of diagnostic processes.66,74,76,77,78,79,80,81,82,83”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [78]
The Introduction (page unavailable) says AI can provide more accurate patient predictions, and the section on non-invasive alternatives describes image-based predictions of cancer subtypes or grades. But the paper does not establish that medical imaging AI significantly improves diagnostic efficiency in clinical practice; its section on prospective clinical trials says real-world added value and effects on clinical workflow still need testing. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDeep learning methods have been developed for automatically extracting morphological features from CT/MRI images and for downstream tasks including lesion detection and segmentation, image enhancement and reconstruction, sample classification, and prognosis prediction.
“Deep learning methods have been developed for automatically extracting the morphological features of these images and for downstream tasks of lesion detection and segmentation, image enhancement and reconstruction, sample classification, and prognosis prediction [100].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [100]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredAssimilating multimodal data can boost AI systems' robustness and accuracy, enable discovery of new biomarkers and therapeutic targets, and improve model performance.
“This can boost their robustness and accuracy, enable the discovery of new biomarkers and therapeutic targets6,14, as well as improve model performance15–17.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [6]
AI systems can profit significantly from assimilating multimodal data into prediction and classification models to imitate integrative human clinical decision-making.
“AI systems can profit significantly from assimilating multimodal data into prediction and classification models to imitate integrative human clinical decision-making.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [6]
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 [27]
Currently, only a few studies on multimodal AI have made an effort to make their systems explainable.
“Currently, only a few of these studies on multimodal AI have made an effort to make their systems explainable, even though the importance of multimodal XAI systems has been highlighted6.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [6]
The “Multimodal Interpretability” section calls interpretability crucial and describes methods for multimodal models, but does not say that only a few studies have tried to make their systems explainable. Searches for few studies incorporating interpretability or explainability found no passage establishing that claim (page numbers unavailable in our copy). (LLM, conf=0.74)
Experimental — accuracy not yet measuredNo 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 [27]
The Abstract describes a synopsis of multimodal AI methods in oncology, not an assessment of reviews in critical care. Searches for critical care, intensive care, and systematic reviews found no passage addressing whether such a review existed. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMultimodal AI systems have been evaluated for various applications in precision oncology, such as outcome predictions.
“Multimodal AI systems have been evaluated for various applications in precision oncology, such as outcome predictions14,15.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [15]
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 [169]
Several multimodal AI systems have been introduced, including models that analyze radiology images together with clinical data or integrate histopathology with genomic or text-based information.
“Recently, several such AI systems have been introduced6. Examples include models that analyze radiology images together with clinical data7 or integrate information from histopathology with genomic8 or text-based information9.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [6]
Multimodal AI platforms incorporate diverse data types to enhance performance of diagnostic and prognostic models.
“Multimodal AI platforms incorporate diverse data types to enhance the performance of these diagnostic and prognostic models.5”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [5]
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- Authors
- 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., Shaban, M., Chen, T. Y., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 40
- Issue
- 10
- Pages
- 1095-1110
- ISSN
- 1535-6108 (ISSN-L)
- PMC
- PMC10655164
- OpenAlex
- W4304014045
- Semantic Scholar
- dc28434a6d10ae9b151dce1fed9591ddfcaf2b81
- Cited by
- 777
- References
- 148
- Citations by year
- 2026: 254 · 2025: 254 · 2024: 178 · 2023: 87 · 2022: 4
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute
- National Institute of General Medical Sciences (R35GM138216, T32CA251062)
- Brigham and Women's Hospital
- National Science Foundation
- Siebel Scholars Foundation
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- multimodal data integration, artificial intelligence in oncology, multimodal fusion, association discovery, interpretability, biomarker discovery, therapeutic target discovery, radiology, histology, genomics, electronic health records, clinical adoption, treatment resistance
- MeSH terms
- Artificial Intelligence, Humans, Medical Oncology, Radiology, Genomics, Electronic Health Records
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.
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