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.
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| http://dx.doi.org/10.7759/cureus.77758 | DOI Listing |
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