Medical Journal of the Islamic Republic Of Iran

en AI‐Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review Artificial intelligence Systematic Review 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.   Artificial Intelligence, Clinical Decision Support Systems, Diagnosis, Treatment, Prognosis, Disease, Prediction 723 749 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-9410-1&slc_lang=en&sid=1 Marzieh Nojomi mnojomi@iums.ac.ir 200319475328460092229 200319475328460092229 No Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences Ebrahim Babaee babaee.e@iums.ac.ir 200319475328460092230 200319475328460092230 Yes Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences Zahra Rampisheh rampisheh@gmail.com 200319475328460092231 200319475328460092231 No Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences Mahshid Roohravan Benis mahshid.roohravan@gmail.com 200319475328460092232 0009-0009-2915-3725 No Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences Mahdi Soheyli mahdisoheyli.ms@gmail.com 200319475328460092233 0009-0002-6543-5075 No Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences Nasibeh Rady Raz radyraz.n@iums.ac.ir 200319475328460092234 200319475328460092234 No Department of Artificial Intelligence in Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran