Artificial Intelligence in Pathology: Present and Future - Saqib Ahmed (Scientific Research, New Technologies and Applications Vol. 3, 2024)
Artificial intelligence is the future and its use in pathology can create a tremendous impact on health care in different aspects. Its use is being initiated in the field of pathology and is on the rise with increasing acceptance. Pathology services will undergo a paradigm shift due to the implementation of computational pathology and the use of AI tools, becoming more effective and able to satisfy the demands of the precision medicine age. Moving AI models from research to clinical applications has been sluggish, not standing their success. There may be too much distance and neglect between the clinical setting and self-contained research. The merge of AI technologies into pathology has significantly impacted diagnostic precision and speed. Digital pathology platforms equipped with machine learning algorithms enable pathologists to analyze large volumes of histological images with enhanced accuracy. These systems have demonstrated remarkable capabilities in identifying subtle morphological features indicative of various diseases such as cancerous lesions or infectious conditions. Moreover, AI-driven image analysis tools can assist pathologists in differentiating between benign and malignant tumors by quantifying cellular characteristics beyond human visual perception. Furthermore, AI-powered predictive models have the potential to refine prognostic assessments based on pathological findings. By leveraging vast datasets encompassing clinical outcomes and molecular profiles associated with specific diseases or tissue alterations, these algorithms can generate more tailored predictions regarding disease progression or treatment responsiveness. Through this approach, pathologists can offer more precise guidance on patient management while harnessing valuable insights from diverse sources for optimizing therapeutic intervention. The convergence of advanced image recognition techniques, virtual microscopy, and genomics data analysis could enable comprehensive profiling of individual disease phenotypes at an unprecedented level. In conclusion, AI technologies have already begun reshaping the landscape of modern pathology practices through improved diagnostic capabilities, enriched prognostic insights and envisaged pathways toward personalized healthcare delivery. The seamless integration of AI-driven solutions into daily laboratory workflows will undeniably propel pathology into a new era marked by heightened efficiency and unparalleled precision in diagnostics and therapeutic support.