ReviewMedical journal of the Islamic Republic of Iran2025
AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.
Review in Medical journal of the Islamic Republic of Iran, 2025. 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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is transforming healthcare with applications that can surpass human performance in prevention, detection, and treatment. This systematic review aimed to collect and assess the impact and success of AI technologies across various healthcare domains. Methods: A systematic search of major databases (including PubMed, Scopus, and ISI) was conducted for articles published up to 2023. Keywords related to AI-driven disease detection, classification, and prognosis were used. Non-English articles or those with inaccessible full texts were excluded. Data was extracted by two researchers, and the quality of selected articles was evaluated based on the strengths and limitations stated by the authors. Results: In total, 123 articles were included. AI contributions were categorized into three areas. For disease detection (n=75), Coronavirus disease 2019 (COVID-19) was the most frequent topic (n=18), followed by oncology. Chest X-rays were the most common input (n=15). In disease classification (n=23), oncology (especially breast cancer) was the most researched field (n=7), primarily using breast imaging. For prediction and prevention (n=25), oncology was again the most studied category, with clinical and laboratory parameters being the most utilized input (n=12). Conclusion: AI-driven clinical decision support systems (CDSS) exhibit strong diagnostic and prognostic accuracy in imaging and laboratory settings. However, many models function as "black boxes," which limits interpretability and clinician trust. Data bias and challenges in integrating AI tools into practice also persist. The findings suggest that future work should focus on explainable AI and rigorous real-world validation to safely implement these tools in healthcare.
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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.