ReviewCureus2025
Leveraging Artificial Intelligence to Predict and Manage Complications in Patients With Multimorbidity: A Literature Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- The future of multimorbidity management in the older adults: transforming AI-enabled precision medicine.Frontiers in public health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is revolutionizing healthcare by improving diagnostic accuracy, streamlining treatment protocols, and augmenting patient care, especially in the management of multimorbidity. This review assesses the applications of AI in forecasting and controlling problems in multimorbid patients, emphasizing predictive analytics, real-time data integration, and enhancements in diagnostics. Utilizing extensive datasets from electronic health records and medical imaging, AI models facilitate early complication prediction and prompt therapies in diseases such as cancer, cardiovascular disorders, and diabetes. Notable developments encompass AI systems for the diagnosis of lung and breast cancer, markedly decreasing false positives and minimizing superfluous follow-ups. A comprehensive literature search was performed via PubMed and Google Scholar, applying Boolean logic with keywords such as "artificial intelligence", "multimorbidity", "predictive analytics", "machine learning", and "diagnosis". Articles published in English from January 2010 to December 2024, encompassing original research, systematic reviews, and meta-analyses regarding the use of AI in managing multimorbidity and healthcare decision-making, were included. Studies not pertinent to therapeutic applications, devoid of outcome measurements, or restricted to editorials were discarded. This review emphasizes AI's capacity to augment diagnostic precision and boost clinical results while also identifying substantial hurdles, including data bias, ethical issues, and the necessity for rigorous validation and longitudinal research to guarantee sustainable integration in clinical environments. This review's limitations encompass the possible exclusion of pertinent studies due to language and publication year constraints, as well as the disregard for grey literature, potentially constraining the comprehensiveness of the findings.
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.