Evaluating Clinical Effectiveness of AI-Based Diagnostics: A Review of Real-World Evidence and Trials in Healthcare

Abstract

Artificial Intelligence based diagnostics have changed modern healthcare by improving the accuracy and efficiency of disease diagnosis in various specialties. The AI methods used many advanced algorithms and data analytics in order to make better clinical decision making and patient outcomes. This review study is very useful in current era as it addresses the issues related to urgent need to evaluate the real-world effectiveness of AI techniques and its integration into clinical processes. It also shows how AI support to fill the healthcare gaps, , especially in regions with limited resources. A systematic review process is applied, extracting evidence from clinical trials, observational cases, and real-world applications to evaluate AI based diagnostic accuracy, efficiency with privacy.  The review also examines multidisciplinary performance and insights in several medical fields.  The review also explores many interdisciplinary insights and performance metrics in different healthcare sectors. This study found that AI significantly improve diagnostic capabilities in radiology, oncology, cardiology, ophthalmology, and neurology, supporting early detection diseases and personalized care. It also highlights the AI’s capabilities in handling diagnostic errors and improving the diagnostic workflow optimization. The study alert to the research communities regarding the challenges like algorithmic bias, data quality, regulatory hurdles, and lack of standardization, that must be addressed by farming ethical policies and frameworks, robust validation. The study put light on the future directions for longitudinal, multi-center trials and continuous clinician training to ensure safe and equitable AI deployment. This review study serves as a comprehensive resource for researchers, physicians, and decision makers, offering significant insights into the clinical utility and effectiveness of AI diagnostics and contributing to the evolution of data-driven, AI based healthcare..