Artificial Intelligence in Healthcare Review Highlights Clinical Applications, Implementation Strategies, and Challenges

Artificial Intelligence in healthcare has been reviewed in a narrative study that surveys recent clinical applications across multiple specialties. The paper examines how AI tools are integrated into diagnostic workflows and patient management. The paper also discusses the role of AI in improving diagnostic accuracy.

Clinical Applications highlighted include image analysis for radiology, predictive analytics for patient risk stratification, and natural language processing for electronic health records. The review notes improvements in accuracy and efficiency reported in several trials. It cites specific studies where AI reduced diagnostic time by up to 30%.

Implementation Strategies discussed involve multidisciplinary collaboration, data governance frameworks, and clinician training programs. The authors emphasize the importance of aligning AI solutions with existing clinical protocols to ensure smooth adoption. The authors also recommend establishing cross‑functional teams to oversee AI integration.

Challenges identified encompass data quality issues, algorithm transparency, regulatory approval hurdles, and concerns about bias and equity. The study calls for standardized evaluation metrics and ongoing post‑deployment monitoring. They highlight the need for transparent reporting of algorithm performance metrics.