Australia has the highest incidence of skin cancer globally. Early detection and treatment of skin cancer is critical for positive patient outcomes. General practitioners (GPs) play a central role in skin cancer management in Australia.
Collaboration between GPs and pathologists can improve the accuracy of skin cancer diagnosis. However, for improvement to occur, clear communication and high-quality specimens are essential.
Inadequate clinical information and suboptimal biopsy specimens can hinder diagnosis. Improved communication, targeted training and selecting appropriate biopsy techniques are essential. A collaborative approach, guided by recommended techniques and clear guidelines, can minimise errors and improve patient outcomes in Australia\'s GP-led skin cancer management system.

The uncertainty of true labels in medical images hinders diagnosis owing to the variability across professionals when applying deep learning models. We used deep learning to obtain an optimal convolutional neural network (CNN) by adequately annotating data for oral exfoliative cytology considering labels from multiple oral pathologists. Six whole-slide images were processed using QuPath for segmenting them into tiles. The images were labeled by three oral pathologists, resulting in 14,535 images with the corresponding pathologists\' annotations. Data from three pathologists who provided the same diagnosis were labeled as ground truth (GT) and used for testing. We investigated six models trained using the annotations of (1) pathologist A, (2) pathologist B, (3) pathologist C, (4) GT, (5) majority voting, and (6) a probabilistic model. We divided the test by cross-validation per slide dataset and examined the classification performance of the CNN with a ResNet50 baseline. Statistical evaluation was performed repeatedly and independently using every slide 10 times as test data. For the area under the curve, three cases showed the highest values (0.861, 0.955, and 0.991) for the probabilistic model. Regarding accuracy, two cases showed the highest values (0.988 and 0.967). For the models using the pathologists and GT annotations, many slides showed very low accuracy and large variations across tests. Hence, the classifier trained with probabilistic labels provided the optimal CNN for oral exfoliative cytology considering diagnoses from multiple pathologists. These results may lead to trusted medical artificial intelligence solutions that reflect diverse diagnoses of various professionals.

OBJECTIVE: We sought to investigate the adoption and perception of large language model (LLM) applications among pathologists.
METHODS: A cross-sectional survey was conducted, gathering data from pathologists on their usage and views concerning LLM tools. The survey, distributed globally through various digital platforms, included quantitative and qualitative questions. Patterns in the respondents\' adoption and perspectives on these artificial intelligence tools were analyzed.
RESULTS: Of 215 respondents, 100 (46.5%) reported using LLMs, particularly ChatGPT (OpenAI), for professional purposes, predominantly for information retrieval, proofreading, academic writing, and drafting pathology reports, highlighting a significant time-saving benefit. Academic pathologists demonstrated a better level of understanding of LLMs than their peers. Although chatbots sometimes provided incorrect general domain information, they were considered moderately proficient concerning pathology-specific knowledge. The technology was mainly used for drafting educational materials and programming tasks. The most sought-after feature in LLMs was their image analysis capabilities. Participants expressed concerns about information accuracy, privacy, and the need for regulatory approval.
CONCLUSIONS: Large language model applications are gaining notable acceptance among pathologists, with nearly half of respondents indicating adoption less than a year after the tools\' introduction to the market. They see the benefits but are also worried about these tools\' reliability, ethical implications, and security.

BACKGROUND: The ultimate aim of medical education is to produce successful practitioners, which is a goal that educators, students and stakeholders support. These groups consider success to comprise optimum patient care with consequently positive career progression. Accordingly, identification of the common educational features of such high-achieving doctors will facilitate the generation of clinical excellence amongst future medical trainees. In our study we source data from British clinical merit award schemes and subsequently identify the medical school origins of pathologists who have achieved at least national distinction.
METHODS: Britain operates Distinction Award/Clinical Excellence Award schemes which honour National Health Service doctors in Scotland, Wales and England who are identified as high achievers. This quantitative observational study used these awards as an outcome measure in an analysis of the 2019-20 dataset of all 901 national award-winning doctors. Where appropriate, Pearson\'s Chi-Square test was applied.
RESULTS: The top five medical schools (London university medical schools, Aberdeen, Edinburgh, Oxford and Cambridge) were responsible for 60.4% of the pathologist award-winners, despite the dataset representing 85 medical schools. 96.4% of the pathologist merit award-winners were from European medical schools. 9.0% of the pathologist award-winners were international medical graduates in comparison with 11.4% of all 901 award-winners being international medical graduates.
CONCLUSIONS: The majority of pathologists who were national merit award-winners originated from only five, apparently overrepresented, UK university medical schools. In contrast, there was a greater diversity in medical school origin among the lower grade national award-winners; the largest number of international medical graduates were in these tier 3 awards (13.9%). As well as ranking educationally successful university medical schools, this study assists UK and international students, by providing a roadmap for rational decision making when selecting pathologist and non-pathologist medical education pathways that are more likely to fulfil their career ambitions.

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Prostate cancer stands as the most prevalent malignant tumor among men; with its incidence increasing with advancing age. The spectrum of patient care options for this disease is broad, encompassing approaches such as \"active surveillance,\" definitive radiation therapy, robot-assisted surgery, among others. These diverse modalities afford opportunities for cure or successful management in the majority of cases. It is paramount to underscore that optimal treatment hinges upon a multidisciplinary framework, wherein the coordinated efforts of allied healthcare professionals yield the highest standard of patient care. Hence, it is imperative for pathologists to keep abreast of contemporary processing and specimen collection protocols, as well as the potential necessity of supplementary investigations and their clinical significance. The latest Hungarian guideline on prostate cancer care features a dedicated chapter delineating the pivotal role and responsibilities of pathologists. Through this discourse, we aim to consolidate and disseminate pertinent insights, thereby fostering the continuing enhancement of pathologists\' knowledge and elucidating the intricacies of histological processing to our clinical counterparts.

OBJECTIVE: Over 50% of breast cancer cases are \"Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)\", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study we evaluated the performance of a deep-learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells\' staining intensity and completeness of membrane staining.
RESULTS: We trained a DL model on a multicentric cohort of breast cancer cases with HER2-IHC scores (n = 299). The model was validated on two independent multicentric validation cohorts (n = 369 and n = 92), with all cases reviewed by three senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. The model\'s concordance with the ground truth (ICC = 0.77 [0.68-0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the three senior pathologists (ICC = 0.45 [0.17-0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93% - 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumours, respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumour-infiltrating lymphocytes, and necrosis, whilst some artefacts such as nonspecific background cytoplasmic stain in the cytoplasm of tumour cells also cause discrepancy.
CONCLUSIONS: Deep learning can support pathologists\' interpretation of difficult HER2-low cases. Morphological variables and some specific artefacts can cause discrepant HER2-scores between the pathologist and the DL model.

The objective analysis of state of medical personnel, along with assessment of real need for specialists, is the basis of improving activities of any health care service. In relation to pathologists, there is unique opportunity to perform similar analysis, based on application of current corresponding staff standards that consider volume of workload of physicians in order to determine required number of positions. The implementation of corresponding original methodology permitted to establish that the actual number of staff positions of pathologists in 2022 amounted up to average 40.6% of the number required according to staffing standards in the Irkutsk Oblast. The physician staffing ratio, calculated on the basis of required number of positions found according to proposed methodology, decreases to 29.1% and staffing with physicians excluding combined jobs to 17.1%. At that, implemented workload per one pathologist reaches 5.9 of standard positions. The deficiency of representatives of this specialty in the region, even if current combined jobs ratio is maintained, is 154 specialists.

OBJECTIVE: Human epidermal growth factor receptor 2 (HER2) expression is an important biomarker in breast cancer (BC). Most BC cases categorised as HER2-negative (HER2-) express low levels of HER2 [immunohistochemistry (IHC) 1+ or IHC 2+/in-situ hybridisation not amplified (ISH-)] and represent a clinically relevant therapeutic category that is amenable to targeted therapy using a recently approved HER2-directed antibody-drug conjugate. A group of practising pathologists, with expertise in breast pathology and BC biomarker testing, outline best practices and guidance for achieving consensus in HER2 IHC scoring for BC.
RESULTS: The authors describe current knowledge and challenges of IHC testing and scoring of HER2-low expressing BC and provide best practices and guidance for accurate identification of BCs expressing low levels of HER2. These expert pathologists propose an algorithm for assessing HER2 expression with validated IHC assays and incorporate the 2023 American Society of Clinical Oncology and College of American Pathologist guideline update. The authors also provide guidance on when to seek consensus for HER2 IHC scoring, how to incorporate HER2-low into IHC reporting and present examples of HER2 IHC staining, including challenging cases.
CONCLUSIONS: Awareness of BC cases that are negative for HER protein overexpression/gene amplification and the related clinical relevance for targeted therapy highlight the importance of accurate HER2 IHC scoring for optimal treatment selection.

Complex in vitro models (CIVMs) offer the potential to increase the clinical relevance of preclinical efficacy and toxicity assessments and reduce the reliance on animals in drug development. The European Society of Toxicologic Pathology (ESTP) and Society for Toxicologic Pathology (STP) are collaborating to highlight the role of pathologists in the development and use of CIVM. Pathologists are trained in comparative animal medicine which enhances their understanding of mechanisms of human and animal diseases, thus allowing them to bridge between animal models and humans. This skill set is important for CIVM development, validation, and data interpretation. Ideally, diverse teams of scientists, including engineers, biologists, pathologists, and others, should collaboratively develop and characterize novel CIVM, and collectively assess their precise use cases (context of use). Implementing a morphological CIVM evaluation should be essential in this process. This requires robust histological technique workflows, image analysis techniques, and needs correlation with translational biomarkers. In this review, we demonstrate how such tissue technologies and analytics support the development and use of CIVM for drug efficacy and safety evaluations. We encourage the scientific community to explore similar options for their projects and to engage with health authorities on the use of CIVM in benefit-risk assessment.