ReviewBJR artificial intelligence2024
Digital pathology and multimodal learning on oncology data.
Review in BJR artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Review
- Reliable Radiologic Skeletal Muscle Area Assessment-A Biomarker for Cancer Cachexia Diagnosis.Cells · 2026Article
- Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight.Briefings in bioinformatics · 2026Review
- UroFusion-X: a unified multimodal deep learning framework for robust diagnosis, subtyping, and prognosis of urological cancers.NPJ digital medicine · 2026Article
- Matched MRI, Segmentations, and Histopathologic Images of Brain Metastases from Primary Lung Cancer.Scientific data · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- The AI-powered pathologist: A global survey mapping initial trends in AI adoption and outlook.Journal of pathology informatics · 2025Article
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- Multimodal Integration Enhances Tissue Image Information Content: A Deep Feature Perspective.Bioengineering (Basel, Switzerland) · 2025Article
- Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Review
- Reliable Radiologic Skeletal Muscle Area Assessment - A Biomarker for Cancer Cachexia Diagnosis.medRxiv : the preprint server for health sciences · 2025Article
- REASONING BEYOND ACCURACY: EXPERT EVALUATION OF LARGE LANGUAGE MODELS IN DIAGNOSTIC PATHOLOGY.medRxiv : the preprint server for health sciences · 2025Article
- Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation.Frontiers in oncology · 2025Review
- Multimodal learning for scalable representation of high-dimensional medical data.Frontiers in digital health · 2025Article
- Vision-language models for medical report generation and visual question answering: a review.Frontiers in artificial intelligence · 2024Review
- Deep Learning in Cytopathology: The Potential for Multimodal Synergy of Clinical and Cytological Input Data.Journal of cytologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer presents a complex tapestry of biological, clinical, and molecular characteristics that collectively influence its diagnosis, progression, and treatment. This review article delves into the recent advancements in integrating multimodal oncology data, a pivotal aspect in the evolving field of digital pathology (DP). The complexity and heterogeneity of cancer, characterized by its multimodal nature, present significant challenges in diagnosis and treatment. Traditional methods of oncology data analysis often fall short of capturing the comprehensive landscape of cancer's multifaceted characteristics. The advent of artificial intelligence, machine learning, and deep learning has marked a new era in multimodal learning. These technologies have revolutionized how oncologists and researchers understand and approach cancer, allowing for a more nuanced and detailed analysis. In this review article, we attempt to examine and present how DP enriches its methods and analysis with other data modalities, including clinical, radiological, and molecular information. We present opportunities and challenges of multimodal learning in oncology, highlighting the synergistic potential of combining various data types for improving cancer care with a focus on DP. Continuous innovation in multimodal data integration will be instrumental in transforming cancer diagnosis, treatment planning, prognosis, and post-treatment surveillance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.