: With an increasing demand for accuracy and efficiency in diagnostic pathology, computer-assisted diagnosis (CAD) emerges as a prominent and transformative solution. This review aims to explore the practical applications, implications, strengths, and weaknesses of CAD applied to diagnostic pathology. A comprehensive literature search was conducted to include English-language studies focusing on CAD tools, digital pathology, and Artificial intelligence (AI) applications in pathology. The review underscores the transformative potential of CAD tools in pathology, particularly in streamlining diagnostic processes, reducing turnaround times, and augmenting diagnostic accuracy. It emphasizes the strides made in digital pathology, the integration of AI, and the promising prospects for prognostic biomarker discovery using computational methods. Additionally, ethical considerations regarding data privacy, equity, and trust in AI deployment are examined. CAD has the potential to revolutionize diagnostic pathology. The insights gleaned from this review offer a panoramic view of recent advancements. Ultimately, this review aims to guide future research, influence clinical practice, and inform policy-making by elucidating the promising horizons and potential pitfalls of integrating CAD tools in pathology.
Computer-assisted diagnosis to improve diagnostic pathology: A review
Merolla, Francesco;
2025-01-01
Abstract
: With an increasing demand for accuracy and efficiency in diagnostic pathology, computer-assisted diagnosis (CAD) emerges as a prominent and transformative solution. This review aims to explore the practical applications, implications, strengths, and weaknesses of CAD applied to diagnostic pathology. A comprehensive literature search was conducted to include English-language studies focusing on CAD tools, digital pathology, and Artificial intelligence (AI) applications in pathology. The review underscores the transformative potential of CAD tools in pathology, particularly in streamlining diagnostic processes, reducing turnaround times, and augmenting diagnostic accuracy. It emphasizes the strides made in digital pathology, the integration of AI, and the promising prospects for prognostic biomarker discovery using computational methods. Additionally, ethical considerations regarding data privacy, equity, and trust in AI deployment are examined. CAD has the potential to revolutionize diagnostic pathology. The insights gleaned from this review offer a panoramic view of recent advancements. Ultimately, this review aims to guide future research, influence clinical practice, and inform policy-making by elucidating the promising horizons and potential pitfalls of integrating CAD tools in pathology.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
