AI-POWERED SOLUTIONS FOR MEDICAL IMAGE ANALYSIS AND DIAGNOSIS

Abstract

Background: Advanced intelligent methods are revolutionizing image and diagnostic processes in medicine making them more accurate, efficient, and reliable. However, most AI models are still relatively new, so the relationships between the AI model performance, data quality, integration of clinicians into the algorithm, decision-making speed and process, and diagnostic accuracy are not fully understood.

Objective: Thus, the purpose of this study is to test the Hypothesis: There is a positive correlation between AI model efficiency performance and data quality and clinician integration, which significantly influences diagnostic accuracy (the dependent variable) and is moderated through decision-making efficiency in the sphere of medical imaging supported by artificial intelligence.

Methods: This was a descriptive-exploratory cross-sectional, community-based survey conducted among 355 healthcare workers. Several Likert-scaled items that captured the important variable were used in the survey. Regarding statistical analysis, descriptive analysis, the reliability of the study with Cron luck alpha, normality test, regression analysis, and mediation analysis were performed.

Results: High mean scores (e.g., diagnostic accuracy: 4.3) and high t-value/probable cause coefficients (+, − 5.67/, − 3.95; R² = 0.68) proved the positive outcomes of AI-based solutions on the diagnostic reliability. Decision-making efficiency emerged as a significant mediator, supporting the idea of the importance of integrating AI into clinical practices. Assessment of internal consistency for the reliability testing was very high at Cronbach’s alpha coefficient of 0.85. Other issues, for example, data quality and clinicians’ uptake were also noted.

Conclusion: Advanced specific applications related to medical imaging reveal that artificial intelligence produces improved diagnostic precision and shorter time taken to make decisions. Further optimization of its capabilities will be possible through addressing challenges arising from data quality issues and clinician training, as well as addressing ethical considerations. This paper offers practical recommendations for applying AI in clinical settings, opening the door for higher, faster, and fairer standards for serving patients.