ABSTRACT
Background: The integration of artificial intelligence (AI) and machine learning (ML) algorithms into healthcare has revolutionized disease diagnosis. These technologies enhance diagnostic accuracy by analyzing medical images and patient data, enabling early detection and improved clinical outcomes.
Aim: This study aimed to examine the transformative role of AI and ML algorithms in disease diagnosis, focusing on their ability to analyze medical images and patient data with greater accuracy compared to conventional diagnostic methods.
Methods: A prospective study was conducted at Mayo Hospital Lahore from October 2023 to September 2024, involving a study population of 50 participants. The study utilized a combination of retrospective patient data and medical images processed using advanced AI and ML algorithms. The diagnostic performance of these algorithms was evaluated against standard clinical practices, with metrics such as sensitivity, specificity, and accuracy serving as benchmarks.
Results: AI and ML algorithms demonstrated significantly higher diagnostic accuracy (94%) compared to conventional methods (85%). Sensitivity and specificity were 92% and 96%, respectively. The algorithms excelled in identifying subtle abnormalities in medical images, reducing diagnostic time by 40%, and improving interobserver consistency among clinicians. Furthermore, their application in patient data analysis highlighted potential risk factors, offering personalized diagnostic insights.
Conclusion:
The findings underscored the transformative potential of AI and ML in disease diagnosis. These technologies not only enhanced diagnostic accuracy but also streamlined clinical workflows, offering a promising avenue for future healthcare innovations. Further research is warranted to optimize their integration into routine clinical practice.
Keywords:
Artificial intelligence, machine learning, disease diagnosis, medical imaging, patient data, diagnostic accuracy, healthcare innovation.