SynthesisOncology2026
From Development to Implementation: A Systematic Review on the Current Maturity Status of Artificial Intelligence Models for Patients with Colorectal Cancer Liver Metastases.
Synthesis in Oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- ASO Author Reflections: Radiomics for Predicting Survival Outcomes After Hepatic Resection of Colorectal Liver Metastases.Annals of surgical oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
<p>Introduction: Artificial intelligence (AI) is increasingly being researched and developed in the medical field and holds the potential to transform healthcare after successful implementation. For patients with colorectal cancer liver metastases (CRLM), many AI models have been developed, but knowledge about translation of these models in the clinical workflow is lacking. Therefore, this systematic review aimed to provide a contemporary overview of the current maturity status of AI models for patients with CRLM.
methodsA systematic search of the literature until November 2, 2023, was conducted in PubMed, <ext-link ext-link-type="uri" xlink:href="http://Embase.com" xmlns:xlink="http://www.w3.org/1999/xlink">Embase.com</ext-link>, and Clarivate Analytics/Web of Science Core Collection to identify eligible studies. Studies using AI and/or radiomics for patients with CRLM were considered eligible. Data on the study aim, study design, size of dataset, country, type of AI application, level of validation and clinical implementation status (NASA technology readiness levels) were collected. Risk of bias and applicability of the individual studies were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).
resultsA total of 117 studies were included. Ninety-seven studies (83%) have been published in the last 5 years. The most common study design was retrospective (96%). Thirty-five studies (30%) utilized a dataset of fewer than 50 patients with CRLM. Internal validation was performed in 63% of the studies and external validation in 17%. The remaining studies did not report validation. Half of the studies were classified as high risk of bias. None of the included studies performed real-time testing, workflow integration, clinical testing, or clinical integration.
conclusionAlthough a rapid increase in research describing the development of AI models for patients with CRLM has been observed in recent years, not a single AI model has been translated into clinical practice. </p>.
Indexed as
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What Socratic holds
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 Socratic graph.