ReviewCureus2026
Artificial Intelligence in Hematologic Malignancies: Opportunities, Challenges, and Clinical Integration.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.Journal of the Egyptian National Cancer Institute · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has shown significant potential in enhancing diagnostic accuracy, refining prognostic models, guiding personalized treatment decisions, and improving clinical workflows. Across the spectrum of blood cancers, AI-based tools have demonstrated strong performance in tasks such as automated image classification, genomic and biomarker analysis, prediction of treatment response and toxicity, and estimation of measurable residual disease. Despite these promising developments, challenges remain, including limited dataset size, lack of prospective validation, concerns regarding interpretability, and ethical considerations related to data privacy and bias. The review emphasizes the need for robust clinical integration strategies, high-quality data, and multiple teams' collaboration to fully harness AI's potential in transforming the management of hematologic malignancies.
Indexed as
Identifiers
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.