Innovation In Radiology: A Comprehensive Systematic Review On The Effectiveness Of Technology In Enhancing Diagnostic Capabilities And Patient Outcomes
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Abstract
Background: The health-care sector is using Artificial Intelligence (AI), Chat GPT, and machine learning (ML) to improve diagnostic accuracy and patient outcomes. These technologies offer new opportunities to enhance precision and effectiveness.
Aim: This review aims to investigate how innovative technologies in radiology improve diagnostic accuracy and efficiency compared to traditional methods.
Method: A comprehensive literature (studies included as n = 11) search was conducted in academic databases including Google scholar, Springer link, PubMed, Wiley Online library, and Science Direct using relevant keywords such as “Using innovative technology in radiology can improve patient outcomes,” “Patient outcome and radiology,” and “radiology technology advancements."
Findings: This approach ensured that important papers published in respected journals were identified and included in the review. Monitoring the impact of AI products in clinical practice is crucial to determine if they improve healthcare in terms of costs and results.
Conclusion: AI has the potential to revolutionize healthcare by enhancing productivity, standardizing quality, and offering accurate prognoses. However, its implementation poses challenges such as uneven technical performance, gaps in acceptance, and disorganized procedures.