ReviewOncogene2023
Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.
Review in Oncogene, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Tumor survivors' musculoskeletal health assessment: advances in imaging biomarkers for cancer treatment-related sequelae.Journal of orthopaedic surgery and research · 2026Review
- Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nature biomedical engineering · 2026Article
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education.NPJ digital medicine · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Mapping the Digital Mind: A Meta-Analysis of EEG Biomarkers in Cognition, Emotion, and Mental Health.Brain sciences · 2026Review
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- "Paradoxical" prognostic role of the TyG index and a novel machine learning-derived nomogram for colorectal cancer liver metastases.Frontiers in nutrition · 2026Article
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence.Frontiers in oncology · 2026Review
- AI driven pre-regulatory validation of PD-L1 analysis in lung cancer.Scientific reports · 2025Article
- Implementing digital pathology: qualitative and financial insights from eight leading European laboratories.Virchows Archiv : an international journal of pathology · 2025Article
- Proof-of-Concept Study: Hyperspectral Imaging for Quantification of DKK-3 Expression in Oropharyngeal Carcinoma.Bioengineering (Basel, Switzerland) · 2025Article
- The impact of artificial intelligence on biomarker discovery.Emerging topics in life sciences · 2025Article
- Comparative performance of PD-L1 scoring by pathologists and AI algorithms.Histopathology · 2025Article
- Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system.Genome medicine · 2025Article
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
- Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.Journal of translational medicine · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice.The Lancet regional health. Europe · 2024Review
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
Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.
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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.