Digital Revolution in Medical Pathology: Integrating Ai, Genomics, and Molecular Imaging

Abstract

The current clinical pathologic diagnostic process using histological slide evaluation demonstrates inconsistent accuracy in medical diagnosis. Progress in digital pathology and other modern technologies now enables the utilization of AI for medical image diagnostics, along with genomic technology for profile assessment and molecular image functionality. This systematic review examines the impact of artificial intelligence technology combined with genomic analysis and molecular imaging systems on present-day pathological medicine advancement. The review includes research published between 2014 and 2025, obtained from the top five databases, to demonstrate how each technology improves diagnosis separately and collaborates for precise medicine advancement. The analysis evaluated ten studies that matched all the established criteria for inclusion. Medical diagnostics benefit from combined system platforms, which also strengthen patient classification systems and treatment selection. However, these platforms require improvements in data standards and workflow connections, as well as computational resources and moral framework requirements. The study defines the necessary criteria for government-connected data platforms and interpretive artificial intelligence models, and then creates regulatory mechanisms in collaboration with interdisciplinary partnerships to develop safe and equitable healthcare applications. A single organized system triggers an essential transformation that shifts pathology from traditional morphological practices toward complex multivariate modern data methods. The research delivers strategic recommendations to enhance future practice and policy development, which will enable these technologies to be widely used in clinical settings.

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