ReviewFrontiers in digital health2026
Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic.
Review in Frontiers in digital health, 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.
- Article
- 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple myeloma (MM) is an incurable hematological malignancy with significant clinical and biological heterogeneity. Despite development and refinement of numerous prognostic models for MM, challenges with accurate and reliable risk stratification remain, highlighted by unexpected, early relapse or progression of disease in patients termed functional high-risk (FHR). To improve decision-making and optimise outcome, there is an unmet need for precise identification of high-risk (HR) patients, to enable tailored therapeutic strategies. With a complex and rapidly evolving treatment landscape, artificial intelligence (AI) and digital twin (DT) technology have emerged as potential tools for personalized medicine in MM. Through the integration and analysis of large data generated in clinical trials, registries and real-world cohorts, AI can inform therapy selection by creating advanced predictive models. DT, virtual patient-specific disease replicas, act as a dynamic, bidirectional bridge between real-world clinical data and computational simulations. Continuous acquisition of patient data, synchronized with DTs through AI-driven architectures, facilitates iterative risk recalibration. This ensures the virtual models accurately reflect evolving disease biology and treatment response. This review provides an overview of current and emerging risk stratification in MM, including genomic-based definitions of HR disease and the concept of FHR MM. We described the role, limitations and controversies of AI and DT in refining risk assessment, their predictive capacity for outcomes and therapy selection. Finally, we provide perspectives on the future of AI application in MM.
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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.