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Review Paper | Health and Medical Sciences | Volume 15 Issue 6, June 2026 | Pages: 1328 - 1330 | India
Live Tissue Metabolic Pathology: Can Cell Metabolism Testing Complement Conventional Tissue Pathology?
Abstract: Traditional tissue pathology relies on examining fixed and stained tissue samples using microscopy in order to diagnose diseases. Recent advances in digital pathology, live-cell imaging, biosensor technology, and Artificial Intelligence have created possibility of studying cellular metabolism in living tissues. This review article evaluates whether live tissue metabolic pathology can replace conventional tissue pathology in future clinical practice. Published studies involving Raman spectroscopy, stain-free microscopy, metabolic imaging, and AI-assisted pathology were reviewed and analyzed. Existing literature suggests that metabolic pathology may allow earlier disease detection through functional and biochemical analysis before structural abnormalities become visible. Technologies such as Raman Spectroscopy and AI-based computational systems have demonstrated promising applications in cancer diagnosis, live-cell monitoring, and digital pathology workflows. However, several limitations including technical complexity, cost, lack of standardization, and inability to fully replace microscopic tissue morphology remain major barriers. Current evidence suggests that live tissue metabolic pathology is more likely to complement rather than completely replace conventional tissue pathology. Future integration of AI, metabolic imaging, and digital pathology may contribute to the development of "living digital pathology" systems for precision medicine.
Keywords: Medical Sciences, Digital Pathology, Live Metabolism, AI Diagnostics
How to Cite?: Karishma Pilankar, "Live Tissue Metabolic Pathology: Can Cell Metabolism Testing Complement Conventional Tissue Pathology?", Volume 15 Issue 6, June 2026, International Journal of Science and Research (IJSR), Pages: 1328-1330, https://www.ijsr.net/getabstract.php?paperid=MR26521000838, DOI: https://dx.doi.org/10.21275/MR26521000838