Integration of Pathology and Clinical Laboratory Biomarkers for Precision Medicine in Internal Medicine: A Systematic Review

Abstract

Background: Precision medicine in internal medicine increasingly depends on the integration of anatomic pathology, molecular pathology, clinical chemistry, hematology, immunology, microbiology, genomics, proteomics, metabolomics, and digital data. Isolated biomarkers can improve diagnosis or prognosis, but multimodal interpretation may better capture disease heterogeneity and support patient-specific decisions. Objective: To systematically evaluate how pathology-derived and clinical laboratory biomarkers are integrated for precision diagnosis, prognostic stratification, treatment selection, and longitudinal monitoring across major internal-medicine domains. Methods: A PRISMA 2020-structured systematic review was performed across PubMed/MEDLINE, Embase, Scopus, Web of Science, and reference sources. The structured search identified 1,284 records. After removal of 336 duplicates, 948 records were screened and 731 were excluded. Full texts of 217 reports were assessed; 154 were excluded for predefined reasons, leaving 63 studies in the qualitative synthesis. Of these, 51 evaluated clinical laboratory biomarkers, 37 included tissue/pathology or molecular data, and 29 explicitly integrated two or more biomarker modalities. Because biomarker definitions, disease populations, assays, and clinical endpoints were highly heterogeneous, a structured qualitative synthesis was used rather than a single pooled effect estimate. Results: Across cardiovascular, renal, endocrine-metabolic, inflammatory/autoimmune, infectious, hematologic, oncologic, and multisystem disease, integrated biomarker strategies consistently improved biological resolution compared with morphology or a single laboratory analyte alone. High-sensitivity cardiac troponin and natriuretic peptides, kidney injury and filtration markers, glycemic and metabolic phenotypes, inflammatory and immune signatures, molecular pathology, circulating tumor DNA, proteomic/metabolomic profiles, and digital pathology features provided complementary information. The strongest clinical translation was observed when tissue morphology or molecular classification was combined with standardized laboratory tests and validated clinical variables. Major barriers were pre-analytical and analytical variability, inconsistent thresholds, limited external validation, data interoperability, algorithmic bias, cost, and unclear clinical actionability. Conclusion: Integration of pathology and clinical laboratory biomarkers is a core enabling strategy for precision medicine in internal medicine. The most clinically useful models are multimodal but parsimonious, analytically validated, externally validated, interpretable, and linked to a defined clinical decision. Future implementation requires harmonized reporting, interoperable laboratory-pathology data, prospective validation, equity-conscious datasets, and multidisciplinary governance.

Keywords: precision medicine; biomarkers; pathology; laboratory medicine; molecular pathology; multi-omics; clinical chemistry; digital pathology; internal medicine; personalized medicine.