Dermatopathology 2030: Navigating the Digital, Computational and Molecular Convergence for Precision Diagnostics-Originated from a Round Table Discussion at A 66, Basel.
- 1Dermatologic Clinic, Department of Clinical-Surgical, Diagnostic, and Pediatric Science, University of Pavia, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy.
- 2Area de Diagnòstic Anatomopatològic, Hospital Universitari General de Catalunya, Grupo Quirónsalud, Universitat Internacional de Catalunya, 08017 Barcelona, Spain.
- 3Departments of Dermatology and Clinical Pathology, University Hospital of Geneva, 1205 Geneva, Switzerland.
Abstract
Background: Traditional dermatopathology relies on analog, qualitative pattern recognition, which encounters critical limitations when facing diagnostic "gray zones" and ambiguous tumor proliferations. Objectives: This paper synthesizes a comprehensive framework integrating Whole Slide Imaging (WSI), Artificial Intelligence (AI), and multi-omics to evaluate how this structural convergence redesigns diagnostic risk and safety ecosystems toward 2030. Methods: We analyze the conceptual shift from individual microscope-based observation to multiple data integration. We integrate recent algorithmic computational breakthroughs-including virtual histological staining, deep learning margin mapping, and multimodal report generation-and benchmark human inter-observer discordance. These dimensions are contextualized through an index adult case study from the University Hospital of Geneva. Results: Clinical digitization converts qualitative tissue images into quantitative datasets, potentially reducing some sources of individual cognitive error while introducing new systemic vulnerabilities. The clinical necessity of multi-omics is driven by the limitations inherent in human interpretation of morphology. In our index clinical case of a 45-year-old male with a right arm lesion mimicking a seborrheic keratosis, standard morphology and a wide immunohistochemical panel established a diagnosis of melanoma. High-throughput next-generation sequencing (NGS) provided the molecular evidence to refine the biological classification of the tumor through identification of an oncogenic OPTN::RET fusion, supporting a diagnosis of Spitz melanoma according to the 5th edition of the WHO classification. Layering bulk Gene Expression Profiling (GEP) provides an emerging adjunctive prognostic tool to stratify 5-year recurrence risk and manage clinical crossroads regarding Sentinel Lymph Node Biopsy (SLNB) concordance. Conclusions: There is little doubt that AI will improve histopathological workflows, but its potential weaknesses make human supervision mandatory. By the next decade, dermatopathologists must evolve into multimodal biological experts, responsible for integrating morphology, molecular biology, clinical information, and artificial intelligence. Technology may scale information across global networks, but diagnostic safety should continue to depend on human judgment, contextual interpretation, and the ability to navigate uncertainty.
Related
- P049 Navigating diagnostic uncertainty in paraneoplastic large vessel vasculitis
- Navigating Nutrition Information in the Digital Era: How Tourism and Recreation Students Evaluate Online Dietary Advice?
- P088 Navigating large-vessel giant cell arteritis with steroid psychosis, low IgG and bowel obstruction
- Dual PD-1/LAG-3 Blockade in Breast Cancer: Navigating Benefit and Risk.
- The Rainbow After the Storm: Navigating Pregnancy After Perinatal Loss
BibTeX
@article{tomasini2026dermatopathology,
title = {{Dermatopathology 2030: Navigating the Digital, Computational and Molecular Convergence for Precision Diagnostics-Originated from a Round Table Discussion at A 66, Basel.}},
author = {Carlo Tomasini and Maria-Teresa Fernández-Figueras and Gürkan Kaya},
journal = {Dermatopathology (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/dermatopathology13040047},
url = {https://europepmc.org/article/MED/42924768},
note = {CC BY},
}
RIS
TY - JOUR
TI - Dermatopathology 2030: Navigating the Digital, Computational and Molecular Convergence for Precision Diagnostics-Originated from a Round Table Discussion at A 66, Basel.
AU - Carlo Tomasini
AU - Maria-Teresa Fernández-Figueras
AU - Gürkan Kaya
PY - 2026
DA - 2026/10/02
JO - Dermatopathology (Basel, Switzerland)
DO - 10.3390/dermatopathology13040047
UR - https://europepmc.org/article/MED/42924768
AB - Background: Traditional dermatopathology relies on analog, qualitative pattern recognition, which encounters critical limitations when facing diagnostic "gray zones" and ambiguous tumor proliferations. Objectives: This paper synthesizes a comprehensive framework integrating Whole Slide Imaging (WSI), Artificial Intelligence (AI), and multi-omics to evaluate how this structural convergence redesigns diagnostic risk and safety ecosystems toward 2030. Methods: We analyze the conceptual shift from individual microscope-based observation to multiple data integration. We integrate recent algorithmic computational breakthroughs-including virtual histological staining, deep learning margin mapping, and multimodal report generation-and benchmark human inter-observer discordance. These dimensions are contextualized through an index adult case study from the University Hospital of Geneva. Results: Clinical digitization converts qualitative tissue images into quantitative datasets, potentially reducing some sources of individual cognitive error while introducing new systemic vulnerabilities. The clinical necessity of multi-omics is driven by the limitations inherent in human interpretation of morphology. In our index clinical case of a 45-year-old male with a right arm lesion mimicking a seborrheic keratosis, standard morphology and a wide immunohistochemical panel established a diagnosis of melanoma. High-throughput next-generation sequencing (NGS) provided the molecular evidence to refine the biological classification of the tumor through identification of an oncogenic OPTN::RET fusion, supporting a diagnosis of Spitz melanoma according to the 5th edition of the WHO classification. Layering bulk Gene Expression Profiling (GEP) provides an emerging adjunctive prognostic tool to stratify 5-year recurrence risk and manage clinical crossroads regarding Sentinel Lymph Node Biopsy (SLNB) concordance. Conclusions: There is little doubt that AI will improve histopathological workflows, but its potential weaknesses make human supervision mandatory. By the next decade, dermatopathologists must evolve into multimodal biological experts, responsible for integrating morphology, molecular biology, clinical information, and artificial intelligence. Technology may scale information across global networks, but diagnostic safety should continue to depend on human judgment, contextual interpretation, and the ability to navigate uncertainty.
SN - 2296-3529
ER -