Advancing Diagnostic Pathology with Artificial Intelligence: Innovations, Applications, and Future Directions
نویسندگان
1 Ministry of Health, Jazan General Hospital, Saudi Arabia
2 Ministry of Health, PHC Alwaha Hafar AlBatin, Saudi Arabia
3 Ministry of Health, King Fahd Central Hospital, Jazan, Saudi Arabia
4 Ministry of Health, Jazan General Hospital, Saudi Arabia
5 Ministry of Health, Mental health and will complex in Tabuk, Saudi Arabia
6 Ministry of Health, Alyamama hospital, Saudi Arabia
7 Ministry of Health, Al-Qaisumah General Hospital, Saudi Arabia
8 Ministry of Health, Egypt
9 Ministry of Health, Forensic Medical Services Center, Saudi Arabia
10 Ministry of Health, Dammam Medical Complex, Saudi Arabia
11 Ministry of Health, Damamm Regional Laboratory, Saudi Arabia
12 Ministry of Health, PHC Sharg Almohamadih, Saudi Arabia
13 Ministry of Health, PHC Alnjameah Jizan, Saudi Arabia
14 Ministry of Health, Imam Abdulrahman Al Faisal Hospital, Saudi Arabia
15 Ministry of Health, Abu Arish Hospital Lab Speciliest, Saudi Arabia
16 Ministry of Health, Wadi Ad Dawasir Hospital, Saudi Arabia
doi
10.26655/JMCHEMSCI.2024.12.11چکیده
Background and Aim: The integration of artificial intelligence (AI) in pathology has revolutionized diagnostic precision, prognostication, and treatment strategies. Innovations such as machine learning (ML) and deep learning (DL) have enabled automated analysis of histological data, enhancing the accuracy and efficiency of pathology workflows. This article explores the historical milestones, applications, and transformative impact of AI on clinical pathology practices. The aim of this study as to assess the contributions of AI-driven tools in enhancing diagnostic accuracy, workflow efficiency, and prognostic reliability in pathology, with a focus on significant developments, methodologies, and real-world clinical applications.Method: A comprehensive review of advancements in computational pathology was conducted, analyzing studies and trials involving AI technologies. This included ML and DL applications in diagnostic workflows, predictive and prognostic tools, and digital image analysis methods. Case studies and key AI-driven developments, such as convolutional neural networks (CNNs), were also reviewed.Results: AI technologies, such as CNNs, have demonstrated remarkable efficacy in identifying histological patterns, predicting treatment responses, and assessing tumor morphology. Tools like whole-slide image analysis and content-based image retrieval (CBIR) have standardized scoring systems and facilitated rare case diagnoses. Diagnostic accuracy and efficiency improved significantly, with AI complementing human expertise. AI's prognostic models effectively correlated histological features with clinical outcomes, showing potential for wider application.Conclusion: AI has become an indispensable tool in pathology, enhancing diagnostic workflows, improving prognostic accuracy, and standardizing evaluations. While challenges remain, such as validation and diverse demographic applications, the integration of AI tools promises a future of precision and efficiency in pathology. Continued collaboration between pathologists and AI developers is crucial to realize its full potential.
