Artificial Intelligence in Diagnostic and Therapeutic Interventions: A Systematic Review of Randomized Controlled Trials

Jimenez-Flores O.; Pajares-Centeno S.; Mejia-Sanchez O.; Flores-Palacios R. · International Journal of Advanced Computer Science and Applications (2025) · DOI: 10.14569/ijacsa.2025.0160855

Resumen

Artificial intelligence (AI) is increasingly being integrated into diagnostic and therapeutic interventions, offering potential advantages in accuracy, efficiency, and clinical decision-making compared to conventional methods. This systematic review aimed to identify and characterise AI applications assessed in randomised controlled trials (RCTs), and to synthesise the reported clinical outcomes in comparison with standard approaches. A comprehensive search was conducted in PubMed, Scopus, and Web of Science for articles published between 2015 and June 2024. Eligible studies included randomised controlled trials involving patients with various medical conditions who received diagnostic or therapeutic interventions supported by AI technologies. Comparators included conventional diagnostic or treatment methods, placebo, or standard care. Two reviewers independently screened the studies, extracted data, and assessed risk of bias using the Cochrane RoB 1 tool. A total of 13 trials involving 10,566 participants met the inclusion criteria, spanning a range of medical specialties including gastroenterology, dermatology, radiology, oncology, neurology, and ophthalmology. While several trials reported improvements in diagnostic accuracy, treatment planning, or procedural efficiency, other studies showed inconsistent or limited benefits, highlighting the variability in outcomes depending on the clinical context and type of AI application. This review offers an updated synthesis of AI-based clinical interventions evaluated through randomised controlled trials and emphasises the need for further research to validate these tools, standardise their implementation, and assess their broader impact as health technology in modern healthcare systems. © (2025), (Science and Information Organization). All rights reserved.

Instituciones: Universidad Nacional Jorge Basadre Grohmann; Escuela de Posgrado Newman