A Comparative Analysis of Traditional and AI-Driven Methods for Disease Detection: Novel Approaches, Methodologies, and Challenges

Abstract

Background: Accurate and early disease detection is crucial for improving patient outcomes. Traditional methods have relied on manual medical data analysis, which can be labor-intensive and error prone.

Methods: This comparative review examines traditional versus AI-driven detection methods, highlighting their applications, advantages, and limitations. We employed PRISMA guidelines to systematically review the literature, using strict inclusion and exclusion criteria to evaluate relevant studies.

Results: Our findings suggest that while AI-driven methods outperform traditional approaches in terms of speed and accuracy, challenges such as algorithm interpretability and data quality remain significant barriers.

Conclusions: Novel aspects of this study include an in-depth comparison of AI models, their integration into clinical practice, and the challenges of data quality and regulatory frameworks. Overall, AI-driven methods have the potential to revolutionize disease detection, but addressing the challenges of algorithm interpretability and data quality is crucial for their successful integration into clinical practice.