Journal of Pathology Informatics最新文献

{"title":"PATHOS: Pathology attention framework for treatment response stratification in ovarian high-grade serous carcinomas following neoadjuvant chemotherapy on H&E images","authors":"Francesca Miccolis ,&nbsp;Marta Lovino ,&nbsp;Oskari Lehtonen ,&nbsp;Johanna Hynninen ,&nbsp;Sampsa Hautaniemi ,&nbsp;Anni Virtanen ,&nbsp;Elisa Ficarra","doi":"10.1016/j.jpi.2026.100545","DOIUrl":"10.1016/j.jpi.2026.100545","url":null,"abstract":"<div><div>Ovarian high-grade serous carcinoma (ovarian HGSC) is a clinically challenging disease with a poor prognosis, particularly for patients receiving neoadjuvant chemotherapy (NACT) before debulking surgery. In this study, we evaluate the progression-free interval (PFI) after NACT based on hematoxylin and eosin-stained whole-slide images (WSIs) of omental tumor tissue. Digital pathology tools are emerging, aiming at assisting pathologists in diagnosis and analysis; however, distinguishing features associated with response to NACT remain elusive. Multiple instance learning (MIL) coupled with attention mechanisms has shown promise in predicting treatment response from WSIs. Additionally, segmentation tools can identify and delineate regions in WSIs. Whereas some efforts have been made to develop explainable models for clinical outcome, there remains a need for genuinely interpretable models for pathologists. This article introduces the PATHOS framework, a novel approach to explaining crucial features of treatment response based on the PFI time in NACT treated patients from WSIs. PATHOS is composed of three blocks: (1) MIL block to identify informative regions, (2) panoptic segmentation and downstream analysis block for feature computation, and (3) classification block to predict the PFI. The results demonstrate that PATHOS enhances the interpretability of response to NACT in ovarian HGSC patients by highlighting pathologically significant features relevant to PFI prediction, such as tumor cell morphology, stromal abundance, and the spatial distribution of stromal regions. Furthermore, PATHOS identifies approximately 10% of the total WSI area as an informative region for clinical outcome.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100545"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147656619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defining hotspot for estimation of Ki-67 proliferation index of neuroendocrine tumors: QuPath algorithm vs. manual assessment","authors":"Ruben Oganesyan ,&nbsp;Osman Yilmaz ,&nbsp;Yevgen Chornenkyy ,&nbsp;Kanchan Kantekure ,&nbsp;Yuho Ono ,&nbsp;Buthania Hakamy ,&nbsp;Nandan Padmanabha ,&nbsp;Vikram Deshpande ,&nbsp;Raul S. Gonzalez ,&nbsp;Monika Vyas","doi":"10.1016/j.jpi.2026.100655","DOIUrl":"10.1016/j.jpi.2026.100655","url":null,"abstract":"<div><h3>Background</h3><div>Ki-67 proliferation index (PI) is essential for grading well-differentiated neuroendocrine tumors (WD-NETs). Pathologists traditionally assess Ki-67 PI by identifying hotspots and manually counting the positive cells among negative cells, which is expressed as a percentage. We developed an algorithm to objectively determine Ki-67 hotspots and calculate PI in WD-NETs, comparing its results with pathologists' selected hotspots to assess reliability.</div></div><div><h3>Methods</h3><div>Hotspots for gastroenteropancreatic WD-NETs (<em>n</em> = 20) were manually annotated on whole-slide images (WSIs) by six pathologists and compared with algorithm-selected areas. Ki-67 (DAKO, MIB-1 clone, and prediluted) scoring was performed using QuPath's custom object classification algorithm. Pathologists identified hotspots on WSI, captured images, and submitted them for PI determination using the same algorithm. Ki-67 PI was translated to grade per WHO classification (G1: &lt;3%, G2: 3–20%, G3: &gt;20%). A pathologist's consensus grade was determined based on majority pathologist grading (&gt;3/6) for each case. Fleiss's Kappa was used to assess inter-pathologist agreement, Cohen's Kappa was used to evaluate the agreement between pathologists and the algorithm, and Friedman test was used for hotspot area variability analysis.</div></div><div><h3>Results</h3><div>Pathologists showed moderate agreement (Fleiss's Kappa = 0.42, 80% agreement), whereas pathologist–algorithm agreement was fair (Cohen's Kappa = 0.32, 58.9% agreement). Among cases with pathologist consensus grade (<em>n</em> = 19), the algorithm assigned a higher grade in 8 cases (42%). In 60% of cases, hotspots overlapped between methods. There was significant hotspot area variability (Friedman statistic: 95.97, <em>p</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>Manual Ki-67 hotspot assessment is subjective, leading to grading variability. Algorithm-based assessment enhances reproducibility, though this occasionally leads to tumor upgrading, highlighting the need for standardization and further validation.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100655"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How does AI perform compared to human expert panels in medical Delphi studies? A pilot study through the lens of pathology","authors":"Joshua Pantanowitz ,&nbsp;Christopher D. Manko ,&nbsp;John Majewski ,&nbsp;M. Alvaro Berbís ,&nbsp;Alton B. Farris III ,&nbsp;Aly Karsan ,&nbsp;Andrey Bychkov ,&nbsp;Antonio Luna ,&nbsp;Bethany Williams ,&nbsp;Brett Delahunt ,&nbsp;Catarina Eloy ,&nbsp;David S. McClintock ,&nbsp;Eva Hollemans ,&nbsp;J. Mark Tuthill ,&nbsp;Jeroen Van der Laak ,&nbsp;Jerome Y. Cheng ,&nbsp;Jochen K. Lennerz ,&nbsp;John H. Sinard ,&nbsp;Jose Aneiros-Fernandez ,&nbsp;Julien Calderaro ,&nbsp;Hooman Rashidi","doi":"10.1016/j.jpi.2026.100661","DOIUrl":"10.1016/j.jpi.2026.100661","url":null,"abstract":"<div><h3>Background</h3><div>Since their inception, Delphi studies have been a key part of medical literature. They consist of an expert panel tasked with coming to consensus on answers to various questions where obtaining objective results is difficult or impossible, with ranked responses based on a Likert scale. The ability of artificial intelligence (AI), particularly large language models (LLMs), to perform this role traditionally assigned to a panel of experts has been scarcely explored in medicine. This study accordingly aimed to explore the feasibility of an “AI-run” Delphi study applied to the practice of pathology.</div></div><div><h3>Methods</h3><div>A prior human-based Delphi study (PMID: <span><span>36603288</span><svg><path></path></svg></span>) employed to forecast the future role of AI in pathology was repeated, but this time with LLMs (Llama 3, ChatGPT-4, and ChatGPT-3.5 based on availability at the time of the study). This was done at various temperature settings (0, 0.7, and 1.0), a measurement of how much an LLM prioritizes determinism versus creativity. Low temperature caused the models to be more deterministic and focused, whereas high temperature increased creativity. “Delphi-GPT” was created to automate prompts that entailed 5 trials for 180 questions, leading to data that were compared to the original human expert panel.</div></div><div><h3>Findings</h3><div>All LLM and temperature combinations were able to reach consensus for a greater percentage of the 180 questions posed than human experts. Newer ChatGPT-4 and Llama 3 models performed better than ChatGPT-3.5. Whereas AI models and human experts did not always agree, the amount of agreement increased when the temperature setting was increased across all LLMs.</div></div><div><h3>Interpretation</h3><div>LLMs are shown here to successfully be able to simulate a Delphi study in medicine. The data show that generative AI models were consistently able to reach greater degrees of consensus than human experts in their responses to 180 prompts related to the future practice of pathology. This serves as a proof-of-concept that one day, pending further robust methodological validation, AI could even serve as a surrogate for de novo Delphi studies that ordinarily would have relied on feedback from a panel of experts. The reliability of consensus/concordance achieved will depend upon the combination of LLM and temperature setting selected.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100661"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis","authors":"Daiki Taniyama ,&nbsp;Kazuya Kuraoka ,&nbsp;Akihisa Saito ,&nbsp;Aya Kido ,&nbsp;Ayaka Shirai ,&nbsp;Rie Yamamoto ,&nbsp;Masayuki Mano ,&nbsp;Norihiro Teramoto ,&nbsp;Shiro Miura ,&nbsp;Morihisa Akagi ,&nbsp;Norihiko Katayama ,&nbsp;Tomonori Kawasaki ,&nbsp;Chika Nakajima ,&nbsp;Susumu Kikuchi ,&nbsp;Kiyomi Taniyama","doi":"10.1016/j.jpi.2026.100654","DOIUrl":"10.1016/j.jpi.2026.100654","url":null,"abstract":"<div><div>To reflect real-world pathology practice, we developed an artificial intelligence-based pathological computer-aided detection system, trained on diverse epithelial and non-epithelial tumors for gastric biopsy specimens. A multicenter cohort comprising samples from six institutions was used for training and validated with an independent dataset from a seventh institution. We applied two distinct algorithms and three operational validity levels with optimized parameters to address the complexity of pathological diagnosis, reflecting routine diagnostic practice. Our system enabled the detection of malignant regions at low-magnification observation, aligning with the practical workflow of pathologists. In a reader study involving a limited number of test samples, the use of system assistance was associated with an improvement in diagnostic sensitivity. Further analysis revealed that samples with small and dispersed malignant foci had a higher rate of false-negative diagnoses, underscoring the potential of our system to improve diagnostic sensitivity. This study highlights the promise of integrating our system into real-world practice to aid pathologists in the routine diagnosis of gastric biopsy specimens.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100654"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to ‘Developing a smart and scalable tool for histopathological education-PATe 2.0’ Journal of Pathology Informatics 20 (2026) 100535","authors":"Lina Winter ,&nbsp;Annalena Artinger ,&nbsp;Hendrik Böck ,&nbsp;Vignesh Ramakrishnan ,&nbsp;Georgios Raptis ,&nbsp;Bruno Reible ,&nbsp;Jan Albin ,&nbsp;Christoph Brochhausen","doi":"10.1016/j.jpi.2026.100659","DOIUrl":"10.1016/j.jpi.2026.100659","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100659"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing liver fibrosis measurement: Deep learning and uncertainty analysis across multi-center cohorts","authors":"Marta Wojciechowska ,&nbsp;Stefano Malacrino ,&nbsp;Dylan Windell ,&nbsp;Emma L. Culver ,&nbsp;Jessica K. Dyson ,&nbsp;on behalf of the UKAIH consortium,&nbsp;Jens Rittscher","doi":"10.1016/j.jpi.2026.100653","DOIUrl":"10.1016/j.jpi.2026.100653","url":null,"abstract":"<div><div>Digital pathology enables large multi-center studies of histological specimens, but differences in staining protocols and slide quality can compromise the comparability of quantitative results. We analyzed 686 PicroSirius Red-stained liver biopsies from 4 independent cohorts spanning more than 20 clinical sites to assess how stain variability affects automated fibrosis quantification and model uncertainty. An U-Net ensemble was trained to segment collagen and to estimate pixel- and tile-level predictive uncertainty. Across markedly heterogeneous staining conditions, the ensemble achieved strong segmentation performance (Dice 0.83–0.90) and produced informative uncertainty maps that identified artifacts and out-of-distribution regions. Epistemic uncertainty values were typically below 0.002, providing a practical criterion for flagging unreliable predictions. Our results demonstrate that ensemble-based uncertainty estimation complements stain-standardization efforts by quantifying prediction confidence directly from model outputs, improving the reliability and interpretability of collagen proportionate-area measurements across multi-center datasets. This framework supports more trustworthy and reproducible digital-pathology workflows for fibrosis assessment and other histological applications.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100653"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The evolving role of DICOM in digital pathology","authors":"Toby C. Cornish ,&nbsp;Shubham Dayal ,&nbsp;David Ferber ,&nbsp;Joseph Chiweshe ,&nbsp;Robert Monroe","doi":"10.1016/j.jpi.2026.100658","DOIUrl":"10.1016/j.jpi.2026.100658","url":null,"abstract":"<div><div>Digital pathology has emerged as a technology with the potential to transform anatomic pathology by enabling remote consultation, computational analysis, streamlined workflows, and more efficient archival of histopathology slides. Interest in digital pathology has steadily grown as health practitioners increasingly recognize its potential to enhance patient care. In the United States, at least 10 whole slide scanners have been cleared or approved by the Food and Drug Administration (FDA), reflecting a growing confidence in this technology. However, widespread adoption has been hampered by the continued proliferation of proprietary, vendor-specific whole slide image formats that limit interoperability between systems. Whereas commonly used whole slide image formats, such as SVS, iSyntax, BIF, MRXS, and NDPI, have proven adequate for siloed deployments, they are less well-suited for integration into enterprise imaging solutions. The Digital Imaging and Communications in Medicine (DICOM) standard, which revolutionized radiology over three decades ago, offers a vendor-neutral whole slide image format that enables seamless exchange of images and metadata across systems, supporting efficient workflows, accurate diagnosis, and secondary use such as artificial intelligence research. This article discusses the use of DICOM in digital pathology, the key benefits it provides, the clear need for digital pathology vendors to adopt it as a native storage format, and other opportunities and barriers to adoption. As digital pathology matures and computational pathology applications proliferate, DICOM adoption represents a critical step toward an interoperable, vendor-neutral imaging ecosystem.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100658"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational pathology with dynamic convolutional and adaptive kernels","authors":"Taymaz Akan ,&nbsp;Richa Aishwarya ,&nbsp;Md. Shenuarin Bhuiyan ,&nbsp;Steven A. Conrad ,&nbsp;John A. Vanchiere ,&nbsp;Mohammad Alfrad Nobel Bhuiyan","doi":"10.1016/j.jpi.2026.100662","DOIUrl":"10.1016/j.jpi.2026.100662","url":null,"abstract":"<div><div>Data processing and learning have become essential to the advancement of medicine, with pathology and lab medicine being no exception. Integrating scientific research with clinical informatics into clinical practice facilitates novel methodologies for patient care. Computational pathology is a burgeoning subspecialty in pathology that promises a better-integrated solution to histopathological images and clinical informatics. Deep-learning methods in computational pathology have demonstrated considerable advances in automated histopathological image analysis. However, convolutional neural networks (CNNs) face fundamental limitations when dealing with the significant morphological heterogeneity present in disease tissues. Conventional CNNs use fixed convolutional kernels, which restrict their effectiveness in adaptively extracting features from histopathological images that exhibit diverse pathological patterns, staining intensities, and tissue architecture. To address this substantial limitation, we present an optimized variant of Omni-Dimensional Dynamic Convolution (ODConv) networks for distinguishing diseased tissue from healthy tissue. Compared with prior dynamic convolution methods that attend to a single kernel dimension, ODConv applies multi-dimensional attention across spatial positions, input channels, output channels, and kernel candidates, enabling more flexible and adaptive feature extraction. We evaluated our approach on wheat-germ agglutinin-stained and hematoxylin and eosin-stained skeletal muscle images from multiple disease models, including G93A*SOD1 transgenic mice (amyotrophic lateral sclerosis) and Akita mice (Type I diabetes). ODConv, trained entirely from scratch without ImageNet pretraining, achieved competitive classification performance relative to seven fine-tuned pretrained architectures across both staining modalities, demonstrating the effectiveness of omni-dimensional dynamic kernels in learning discriminative morphological representations directly from domain data. The study reports strong statistical agreement metrics, proving effective class balance handling and stable decision boundaries. These findings confirm ODConv as a strong computational pathology framework that advances automated diagnosis of neurodegenerative and metabolic skeletal muscle disorders.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100662"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of macrovesicular steatosis in hematoxylin and eosin-stained histological images of human livers: A feature-based method","authors":"E. Malamutmann ,&nbsp;M. Platte ,&nbsp;S. Theurer ,&nbsp;M. Jalc ,&nbsp;J. Rashidi-Alavijeh ,&nbsp;K. Willuweit ,&nbsp;J.-W. Treckmann ,&nbsp;A. Oezcelik ,&nbsp;F. Nensa ,&nbsp;J. Haubold ,&nbsp;U. Neumann ,&nbsp;D.P. Hoyer","doi":"10.1016/j.jpi.2026.100656","DOIUrl":"10.1016/j.jpi.2026.100656","url":null,"abstract":"<div><h3>Background</h3><div>Macrovesicular steatosis (MaS) affects liver transplant outcomes. Traditional visual biopsy assessment is subjective and shows inter-observer variability (weighted κ = 0.595 in our cohort), which complicates allocation and prognostication.</div></div><div><h3>Methods</h3><div>We developed a semi-automated image analysis method using HALCON Progress (evaluation license, 2019–2023) to quantify MaS in H&amp;E whole-slide images (WSIs). The pipeline processes native SVS files at full resolution (average runtime 1.26 ± 0.53 min/WSI) without tiling, downsampling, or format conversion. Four shape features (area, roundness, circularity, and compactness) guide classification. Results were compared with three specialized pathologists using Pearson and Spearman correlations.</div></div><div><h3>Results</h3><div>Across 129 WSIs (≈52,000 lipid droplets), artificial intelligence–pathologist correlations were statistically significant (Pearson <em>r</em> = 0.526–0.642; Spearman ρ = 0.498–0.615; all <em>p</em> &lt; 0.001; <em>n</em> = 48–95 per pathologist). Correlation with the mean pathologist assessment reached R² = 0.64 (<em>r</em> = 0.80, ρ = 0.782), within the inter-pathologist range (R² = 0.34–0.62; weighted κ = 0.595). Using four shape features, the pipeline separates vacuoles from vessels and processing artifacts during interactive review. Processing native SVS at full resolution avoids extra compute while preserving precision.</div></div><div><h3>Conclusions</h3><div>The method provides a rapid, objective readout of MaS and performs on par with inter-pathologist agreement (R² = 0.64 vs. weighted κ = 0.595). It is best used as decision-support that supplies percentages and overlays for expert review. Main limitations are underestimation in severe, confluent steatosis (&gt;30%), limited coverage of microvesicular fat, and single-center validation. Multi-center evidence across scanners and patient groups is still needed before routine use.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100656"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perceptions of digital pathology among healthcare workers in pathology","authors":"Aditi Tayal,&nbsp;Meghan E. Kapp,&nbsp;Sylvia L. Asa","doi":"10.1016/j.jpi.2026.100652","DOIUrl":"10.1016/j.jpi.2026.100652","url":null,"abstract":"<div><div>The integration of informatics into pathology is rapidly transforming the field, with the potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. Digital pathology, artificial intelligence (AI), and machine learning are among the innovations revolutionizing traditional practices by enabling the digitization of tissue samples, the automation of data analysis, and the incorporation of large datasets into diagnostic processes. While these advancements hold great promise, they have also generated mixed reactions among healthcare professionals, particularly in pathology, where the balance between human expertise and technology is critical. To better understand the perceptions of informatics in pathology, a cross-sectional survey was conducted among healthcare workers within the field. The survey aimed to assess attitudes toward the adoption of informatics, with specific focus on its perceived benefits and challenges. This study seeks to provide insights into how these new technologies are being received by those at the forefront of pathology, exploring both the enthusiasm and apprehension surrounding the growing role of informatics in the discipline.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100652"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147611976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}