ReviewJournal of thrombosis and thrombolysis2026
Innovative strategies for early detection of cardiotoxicity: artificial intelligence and multi-modality collaborative models.
Review in Journal of thrombosis and thrombolysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer therapeutics account for a significant proportion of new drug development, reflecting advances in diagnosis, treatment, and disease control. However, with this trend comes a responsibility to ensure patient safety both in the near term and over the long term. Among these considerations, cardiovascular safety is a priority. Downsizing and resource constraints within regulatory agencies, particularly the United States Food and Drug Administration (FDA) threaten safety oversight capacity, increasing the need for automation, structured data standards, common data elements and shared analytics. Public health policy, public-private partnerships, big data capabilities, and the FDA Oncology Center of Excellence (OCE) provide avenues to strengthen Cardio-Oncology safety through cross-center coordination, technology advances, guidance development, and methodological innovation. In this third offering of a 3-part series, the objective is to outline public-private partnerships and collaborative oversight models, linking sponsors, regulators, health systems, payers, and patient groups to operationalize continuous surveillance, standardized cardiac endpoints, and transparent data sharing. Building on existing scientific and regulatory foundations, there is a pressing need to leverage contemporary technologies and creative, interdisciplinary thinking to advance predictive models, monitoring strategies, and mitigation frameworks. A strategic forecasting and preparedness framework is proposed that includes embedding fit-for-purpose cardiac safety endpoints early in development; interoperable, real-time safety data pipelines across pre- and post-marketing phases; deploying artificial intelligence (AI)-enabled signal detection with human-in-the-loop adjudication; using real world evidence (RWE) to confirm clinical benefit-risk under accelerated approvals; and pre-specifying label-update triggers tied to cardiac signal thresholds. Together, these policy and technology innovations can modernize cardiotoxicity detection, protect patients, and sustain benefits in oncology care.
Indexed as
Identifiers
42213336What 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.