The application of artificial intelligence to improve the access of cancer patients to high-quality medical care is one of the goals of modern medicine. Pathology constitutes the foundation of modern oncologic treatment, and its role has expanded far beyond diagnosis into predicting treatment response and overall survival. However, the funding of pathology is often an afterthought in resource-scarce medical systems. The increased digitalization of pathology has paved the way towards the potential use of artificial intelligence tools for improving pathologist efficiency and extracting more information from tissues. In this review, we provide an overview of the main research directions intersecting with artificial intelligence and pathology in relation to oncology, such as tumor classification, the prediction of molecular alterations, and biomarker quantification. We then discuss examples of tools that have matured into clinical products and gained regulatory approval for clinical use. Finally, we highlight the main hurdles that stand in the way of the digitalization of pathology and the application of artificial intelligence in pathology while also discussing possible solutions.

Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.

Background: Pathology is essential for cancer diagnosis, bridging clinical and surgical fields, and requires adequate infrastructure, technology, and skilled staff to meet standards of care. In Romania, healthcare underfunding limits pathology laboratories’ capacity to provide timely and accurate diagnoses, leading to delays that could negatively impact treatment and patient outcomes. Our study aimed to assess the status of publicly funded pathology laboratories in Romania and identify key areas for improvement. Methods: We analyzed public hospitals in Romania, excluding specialized and non-general care institutions, to evaluate pathology laboratories. A 10-item survey was distributed over 12 months via email, phone, administrative offices, and professional networks to pathologists working in these laboratories, regardless of their hierarchical position. A total of 154 pathology services were represented. The questionnaire assessed technical capabilities, diagnostic techniques, automation, staffing, infrastructure, and satisfaction with funding and resources. Responses were gathered with both predefined and open-text fields to capture comprehensive insights. Results: The findings revealed that many pathology laboratories faced significant challenges, including a lack of automation, limited integration of modern technologies, and barriers to digitalization. Despite these issues, pathologists reported higher-than-expected levels of satisfaction with their laboratories. Conclusions: A comprehensive understanding of existing practices is necessary to drive the modernization of pathology services, establish national standards, and improve collaboration both within and across specialties. Without such foundational insight, efforts to enhance the integration and effectiveness of pathology services are likely to remain constrained.