AI in Pathology: From Traditional Slides to Smart Diagnostics—Progress, Challenges & Promise
Artificial intelligence (AI) is spreading its wings across every sector—and healthcare is no exception. In pathology, AI is making a truly commendable impact by automating and refining the analysis of digitized biopsy slides, enhancing accuracy, efficiency, and patient care.
Many pathologists believe that AI allows for image-based diagnosis on the background of digital pathology, that refers to the environment that includes tools and systems for digitizing pathology slides and associated metadata.
In this article, we explore the significance of AI in pathology starting with the traditional microscope based model and tracing its evolution through computational pathology followed by a discussion of current challenges and a forward looking view of the future of the field.
From Traditional to Modern Approach
Traditionally pathology relied on manual examination of tissue samples through microscopes using hematoxylin and eosin (H&E) staining and other special staining techniques. It resulted in a time-consuming, labor-intensive process and susceptible to inter-observer variability.
With the advent of digital pathology, which involves scanning glass slides into high-resolution wholeslide images (WSI), slides are now managed, analyzed, shared, and interpreted through software rather than a microscope. Compared to traditional static image capture, WSI offers a more comprehensive and widely adopted approach in the digital pathology workflow.
Emergence of Computational Pathology
Digital pathology has given rise to a new subfield known as computational pathology (CPATH). Computational pathology is a branch of pathology that involves computational analysis of a broad array of methods to analyze patient specimens for the study of diseases. This branch involves the application of computational tools and AI algorithms to digitized slides and metadata to analyze disease characteristics more effectively.
Applications of AI in Pathology
Integrating artificial intelligence into the workflow of the pathology department can perform quality control of the pre-analytic, analytic, and post-analytic phases of the pathology department’s work process. Integrating a Laboratory Information System (LIS) with AI-powered software enhances workflow by automatically flagging cases that require immediate attention, populating semi-complete templates for rapid reporting, and generating draft reports informed by past cases and individual reporting preferences. This integration not only saves pathologists valuable time on typing but also helps surface potential errors before finalization—streamlining operations and improving accuracy.
Initially, machine learning approaches in pathology relied on expert pathologists manually extracting features from images. However, the invention of deep learning transformed this process. Unlike traditional methods, deep learning models can automatically extract complex features from pathological images which cannot be noticed by humans, and these can be presented for human interpretation improving diagnostic accuracy and scalability. This shift has not only enhanced diagnostic accuracy and scalability, but it continues to assist pathologists in achieving better results at scale.
Main applications of digital pathology include primary and secondary clinical diagnosis, telepathology, slide sharing, research data set development, and pathology education or teaching. AI tools can even assist pathologists even after case review, helping execute specific tasks such as region annotation or predictive analysis based on patient data.
The application of AI in pathology falls into four main categories such as: diagnostic and workflow applications, prognostic/predictive applications, educational applications, and integration with patient genomic and genetic profiles.
Diagnostic applications: AI excels in diagnostic pathology by efficiently identifying, segmenting, and classifying regions of interest in whole-slide images often achieving expert-level performance. It also effectively quantifies biomarkers, cell counts, tissue architecture, and morphology; standardizes diagnostic scoring; and retrieves similar rare cases via content-based image retrieval (CBIR), enhancing diagnostic support. Overall, this approach streamlines workflows, saves time, and is highly cost-effective.
Prognostic/predictive applications: AI models can predict patient prognosis such as recurrence risk and therapy response based on morphological features from histopathological images, thereby guiding treatment strategies for improved outcomes. These models integrate hundreds or even thousands of image-derived variables into a single prognostic index, surpassing traditional histological grading and manual scoring in accuracy and comprehensiveness.
Educational applications: AI tools are increasingly valuable in pathology training and education. By integrating automated annotations, real-time feedback, and interactive case-based learning into the reporting workflow, these tools significantly enhance trainees’ diagnostic proficiency and engagement.
Integration with patient genomic and genetic profiles: AI in pathology enables models to predict underlying tumor genetic characteristics such as specific mutations and microsatellite instability. However, integrating large-scale genomic data presents several challenges: handling high-dimensional, multimodal datasets, overcoming data scarcity and achieving standardization. Addressing these issues is crucial before AI-driven image–genomic integration can be reliably used in clinical diagnostics.
The integration of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine ensuring the right diagnosis and treatment for the right patient at the right time.
Challenges and Opportunities
While AI models in pathology offer immense potential and advantages, several challenges remain. One major barrier to clinical adoption is the “black box” nature of AI decision-making, which creates a lack of interpretability and trust. To address this, developers are increasingly incorporating end-user feedback into model design to enhance transparency and usability. For AI to be widely accepted, it must demonstrate real clinical value and integrate seamlessly into existing workflows.
Another significant challenge lies in the digital pathology infrastructure, which includes burdensome data storage requirements, high implementation costs, and complex system integration. Ultimately, the adoption of AI in pathology will depend on whether its interpretability, workflow compatibility, and measurable clinical benefit outweigh the infrastructure, integration, and operational challenges.
To summarize, AI in pathology offers a powerful combination of accuracy, scalability, automation, and educational value. Digital and computational pathology enable automated slide analysis, multimodal integration with genomics, and real-time support systems. Its impact spans the entire diagnostic lifecycle from improving routine operations to supporting precision medicine. Realizing this potential will depend on interdisciplinary efforts, technical integration, and regulatory oversight.
Many researchers are convinced that AI in general and deep learning in particular could help with many repetitive tasks using digital pathology because of recent successes in image recognition. The future of AI in pathology will involve real-time diagnostics, multimodal integration with genomic and imaging data, self-learning systems, and global deployment to address equity in care.
With continued interdisciplinary collaboration, technical refinement, and regulatory oversight, AI in pathology is evolving from a promising innovation to the standard of care—empowering clinicians and improving patient outcomes worldwide.