ReviewZhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi2025
[Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies].
Review in Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi, 2025. 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hematological malignancy is a highly heterogeneous disease with complex biological characteristics and diverse clinical manifestations. Therefore, precise diagnosis and treatment are crucial and urgently needed. To further improve the accuracy of diagnosis and prognostication and to promote personalized therapy, artificial intelligence (AI) has been increasingly used. This study reviewed literature published in the last 5 years and summarized the application, benefits, and drawbacks of AI in the diagnosis, treatment, and prognosis of hematologic malignancies. Although AI can effectively improve the accuracy of diagnosis and therapy, low-quality data, poor interpretability of the model, and limited clinical transformation have impeded its popularization and application. In the future, the clinical application of AI in hematologic malignancy can be accelerated by establishing standards for clinical data processing, integrating multimodal information for accurate diagnosis and prognostication, and conducting systematic clinical verification of model algorithms.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.