Strength, weakness, opportunities and challenges (SWOC) experience of histopathology image analysis, enhanced by artificial intelligence

Abstract

Artificial intelligence (AI) is reshaping the landscape of oral cancer diagnosis through the analysis of digital imaging. By promoting early detection, enhancing diagnostic precision, and enabling personalised treatment approaches, AI holds the potential to significantly improve patient outcomes. However, it is important to carefully consider concerns related to bias, costs, data quality, and regulatory standards. Histopathology image analysis is critical for precise and early diagnosis, particularly cancer detection. It improves consistency, decreases subjectivity, and enables accurate assessment. Its combination with AI allows for faster diagnostics, remote consultations, sophisticated research, and personalised treatment methods, making it an essential tool in modern pathology and healthcare. To fully realise its promise in improving patient care and diagnostics for oral cancer, strategic investments, multidisciplinary cooperation, and strong regulatory frameworks are essential. This narrative review highlights the potential and challenges that lie ahead while advocating for a balanced approach that combines technical innovation with ethical and regulatory vigilance based on a comprehensive literature search and our team's personal experience.

1. Introduction

The identification and diagnosis of oral cancer are undergoing a significant transformation with the integration of digital histopathological image analysis.¹ This enhances diagnostic accuracy, promoting early detection and streamlining workflows. As technology continues to evolve, it holds immense promise for improving diagnostic efficiency and ultimately improving patient care and survival outcomes. The transition from conventional microscopy to digital pathology has opened new avenues for image analysis, particularly through the integration of artificial intelligence (AI) and machine learning (ML).^2,3 These technologies rely heavily on large, high-quality annotated datasets to train algorithms for tasks such as automated diagnosis, segmentation, and grading.³ However, image annotation typically performed by expert pathologists remains a significant task in the development of reliable AI models.

Manual annotation is time-consuming and repetitive, also subject to variation by the human, particularly in cases with subtle or complex histological features, as well as uneven staining, slide scanning and image resolution, and the lack of standardized annotation protocols.^4,5 Also, the major challenge is the lack of universal standards for image annotation in pathology, as different platforms and annotation protocols are used by institutions, it eventually affects uniformity and generalizability of data.^5,6

This narrative review endeavours to discuss the evolving role of AI in the analysis of digital histopathological images for the diagnosis of oral cancer. It explores the current strengths, inherent limitations, emerging opportunities, and ongoing challenges (SWOC) associated with the integration of AI into diagnostic pathology. By critically examining existing literature and incorporating clinical insights, this review aims to provide a balanced approach to harness the potential of AI in histopathological image analysis for oral cancer by addressing the ethical, regulatory, and practical considerations that influence its real-world application.

2. Methodology

A literature search using keywords such as “Histopathology image analysis”, “Oral mucosa” and “Artificial intelligence” was conducted using PubMed and Google Scholar databases, Web of sciences including articles published from the year 1992–2025. The articles were screened and only those relevant to the afore-mentioned keywords were included in this narrative review. Additionally, this was reinforced by reviewing the references of pertinent review articles. Publications included original research papers, review or perspective articles in English language. This review emphasizes the transformative role of AI in modern digital pathology by critically evaluating its capabilities and limitations and aims to provide insights into the practical application, current gaps, and future potential of AI-powered histopathology image analysis in oral cancer and other disease diagnostics based on our own experience as well.

3. SWOC analysis

The strengths, weaknesses, opportunities, and challenges (SWOC) associated with histopathological image analysis enhanced by artificial intelligence are illustrated in Fig. 1.

Fig. 1.

SWOC Analysis overview.

3.1. Strengths

• ➢Enhanced Diagnostic Accuracy- One of the most transformative impacts of AI and ML in histopathology is the significant enhancement of diagnostic accuracy. Traditionally, the accuracy of histopathological diagnosis is dependent majorly on the pathologist's expertise, experience, and subjective interpretation of visual patterns under the microscope. Dysplasia encompasses a wide range of epithelial modifications, from mild to severe grade, and its diagnosis is based on a complex interplay of cytological and architectural changes. Several features such as nuclear hyperchromatism, pleomorphism, increased nuclear-to-cytoplasmic ratio, mitotic figures etc. are frequently evaluated semi-quantitatively. However, individual pathologists may interpret these criteria differently depending on their training, experience, and diagnostic threshold. However, AI provides a paradigm shift by enabling highly objective, data-driven evaluations. Using ML algorithms particularly deep learning models like convolutional neural networks (CNNs), AI can rapidly analyse and interpret vast amount of digital image data.⁴ These models are trained on thousands of annotated histological images and enabling after training phase to identify morphological changes, cellular abnormalities, and tissue architectural patterns that may be difficult for identification through human eyes. Also, the diagnostic precision can be improved through quantitative image analysis, allowing for accurate measurement of features such as nuclear size, shape irregularities, mitotic activity, and staining intensity. These metrics are critical in grading tumors, assessing margins, and determining the aggressiveness of pathological changes.^4,6,7 Most importantly, AI tools do not replace human expertise but rather augment the pathologist's capabilities, serving as an adjunct to enable faster, more accurate, and more consistent diagnoses, which ultimately adds objectivity and helps to overcome subjectivity thereby, contributing to improved patient management, timely treatment, and better clinical outcomes.
• ➢Reproducibility & Standardization- Training and validation of the automated AI models by image datasets facilitates reducing variability; automatic scoring systems ensure that values remain uniform among institutions and thus for reliable comparison and benchmarking. Feature detection and scoring of feature classifiers such as cell atypia, nuclear-cytoplasmic ratio, mitotic figures and architectural patterns can be automatically detected, measured and scored according to predefined computational criterias. By providing reproducible and consistent feature extraction, AI will minimize the risk of human variability and increase the reliability of diagnosis.⁸

In particular, scoring systems developed using AI enable consistent grading of tumors. By fostering collaboration across institutional boundaries in image interpretation and scoring, AI actively promotes the alignment and standardization of diagnostic procedures, supports quality control, and strengthens evidence-based practices in pathology. This coordinated approach enhances the consistency and reliability of diagnoses, improves treatment planning, and facilitates more effective multi-center collaboration in both clinical and research settings for implementation of global diagnostic standards.^8,9

• ➢Reduced Time and Workload for Pathologists- One of the most practical benefits of integrating AI into histopathological workflows is the significant reduction in time and workload for pathologists. The conventional approach to histopathological diagnosis requires detailed visual examination of numerous tissue sections under a microscope, often involving tedious, repetitive tasks such as identifying regions of interest (ROIs), counting mitoses, assessing nuclear features, and annotating abnormalities. This process becomes particularly demanding when handling large volumes of datasets. AI driven tool can pre-screen and triage digital whole-slide images (WSIs), automatically identifying and marking regions that need additional expert assessment. High-throughput analysis utilizes algorithms to automatically analyse and allows rapid processing of numerous cases, freeing up pathologist's time for complex and challenging cases. This is achieved by automating tasks like tissue segmentation, feature extraction, and pattern recognition, allowing for faster and more consistent assessments. This technology can significantly improve efficiency in pathology departments by automating and accelerating routine tasks like slide scanning, image analysis, and even biomarker quantification. This decreases the number of slides or regions that must be manually inspected in detail and thereby improving diagnostic efficiency while maintaining accuracy and also facilitates enhanced efficiency, improvised data, enhanced collaboration, improved diagnostic accuracy, digital archiving and cost management.^9,10
• ➢Enhanced Data Management- AI offers profound benefits in the management of histopathological data by streamlining the storage, retrieval, and sharing of digital histopathology images. This facilitates access to large-scale collections of data, facilitates standardization of annotations, and enables collaboration across institutions. AI platforms help to integrate histopathology data with clinical records for long term follow-up studies and advanced researches. This streamlined, scalable approach enhances diagnostic workflow, research productivity, and patient data security.¹¹
• ➢Premalignant analysis and early detection - The integration of artificial intelligence (AI) into histopathological image analysis is emerging as a powerful tool in the early detection and diagnosis of oral premalignant lesions. Premalignant changes can be difficult to detect, even for experienced pathologists. Also, reviewing a large number of slides can lead to fatigue, increasing the likelihood of missing early premalignant changes. However, through proper training of algorithms will enable detection of subtle changes that may be missed by human pathologists. These quantitative metrics not only enhance the objectivity of grading dysplasia but also allow for risk stratification in terms of likelihood of a premalignant lesion progressing to cancer via identification of clinical and histopathological risk factors allowing the clinicians for better management decisions at an early stage. Furthermore, this enables to analyse large volumes of data in a short period of time, making them highly useful for screening programs and high-throughput clinical workflows. In conjunction with digital pathology platforms, AI enables remote consultations, second opinions, and centralized review of complex or ambiguous cases.
• ➢Digital biomarkers - As digital pathology evolves, the synergy between AI and digital biomarkers holds immense potential to drive precision medicine, optimize patient outcomes, and revolutionize how oral potentially malignant disorders (OPMDs) are monitored and managed. Digital biomarkers derived from AI analysis enable early detection of subtle histological changes that may precede visible clinical symptoms or overt malignancy. Second, they allow for risk stratification, helping clinicians prioritize high risk patients for close monitoring or intervention. Thus, by correlating digital image features with molecular markers including p53, Ki-67, or cyclin D1 expression, they provide multimodal diagnostics, offering deeper insights into disease behaviour and prognosis.

3.2. Weaknesses

• ➢High Initial Cost & Resource Requirements- Implementing AI in histopathology demands significant upfront investment in digital scanners, computing infrastructure, and software. Additional costs include training, maintenance, and workflow integration. These requirements pose a challenge, especially for smaller or resource-limited institutions, potentially limiting widespread adoption.
• ➢Data Complexity, Quality Issues and Lack of ecosystem- A major challenge for this approach is that the data itself is highly complex and variable. Histopathology images provide sensitive amounts of cellular and tissue-level information that requires detailed annotation and segmentation. In addition, quality of input data has a direct impact on the performance of an AI system. Variability in staining protocols, tissue preparation methods, section thicknesses etc could lead to differences in the data presented which could confuse the algorithms themselves or hamper the performance of the model.¹²
• ➢Need for Expertise in AI and Pathology- Successful implementation of AI requires a unique balance of pathology, computational science and data scientists and AI engineers. The combination of these expertise enables not only to ensure that the algorithms are technically sound but also clinically relevant and interpretable. Pathologists play an important role in curation, annotation, validation and interpretation of the AI-generated outputs. They allow for an accurate training of the algorithms, reflecting real-world diagnostic tasks and with congruent reference criteria. Without the pathological input of experts in AI systems, the system will be at risk of learning inaccurate or misleading patterns.¹³

As for the technical aspects, the training person must be proficient in ML, deep learning, data pre-processing and image analysis algorithms. It is not often the case in conventional pathology laboratories. Indeed, in many institutions, particularly in low- and middle-income countries as well as smaller academic centers, lack staff in both domains. At present, this represents one of the major limitations on the development of internal AI solutions and even the implementation of commercial solutions to their various challenges. To address this challenge, there is increasing need for capacity-building interventions such as interdisciplinary training programs, workshops and fellowships to train pathologists on AI fundamentals and data scientists on histopathology basics. In addition, supporting the development of collaborative networks between academia, technology companies, and healthcare institutions can speed up innovation while ensuring safe and successful implementation.

• ➢Ethical and Regulatory Concerns- Integrating AI into histopathology introduces ethical challenges, particularly regarding accountability, especially when AI predictions differ from a pathologist's opinion. Furthermore, many AI models have poor explainability making it difficult for clinicians to trust or justify the output received. Bias in training data may also lead to unequal performance across different populations leading to questions about equality. Furthermore, the handling of large amounts of image datasets necessitates strict privacy and data protection policies, and the lack of defined regulatory standards for AI tools in pathology highlights an alarming need for clear guidance on the safe and ethical use of these tools. This necessitates transparent model development, clear accountability systems, strong regulatory supervision, and a dedication to equity and patient rights.^14,15

3.3. Opportunities

• ➢Improving Early Detection & Personalised Medicine: AI can detect minute histological changes that the human eye misses, allowing for earlier detection of Oral Potentially Malignant Disorder's (OPMDs) and cancer. This early detection enables timely intervention, increasing survival rates and minimising disease burden. It promotes personalised medicine by combining morphological traits with molecular profiles to help guide targeted medicines, predict treatment outcomes, and stratify patient risk. Furthermore, AI can help to personalise treatment by analysing patterns associated with patient-specific characteristics such as tumour morphology, progression risk, and treatment response, allowing for precision medicine methods.^2,16
• ➢Integration with Molecular and Genetic Data- Future combining histopathological image analysis with genetic, transcriptomic, proteomic, and metabolomic data to provide a more holistic and multidimensional view of disease. This integration allows for better risk stratification, tumour categorisation, and prognosis. For example, AI could assist in identifying histological correlates of molecular subtypes or predicting gene expression profiles based only on tissue morphology, thereby speeding up biomarker discovery and improving personalised medication decisions.¹⁶

This method allows for the prediction of molecular changes directly from the tissue, improving diagnostic precision and minimising reliance on expensive lab-based procedures. It also increases biomarker discovery and clinical trial efficiency, as well as the development of scalable, interoperable precision diagnostic platforms. This combination of imaging and omics data is a significant step towards comprehensive, data-driven healthcare.

• ➢Expanding Access through Telepathology- Telepathology, when combined with AI, is revolutionising access to histopathological diagnoses by allowing remote access and interpretation of digitised slides. This is especially advantageous in far-fetched rural areas with limited access to experienced oral pathologists. It enables timely diagnoses, second views, and better patient treatment at low research settings and thus facilitating equitable health for all. AI integration extends its utility by automating pre-screening, identifying questionable areas, and lowering diagnostic burden, all of which are critical in low-resource environments. It also plays an important part in medical education, providing remote access to a variety of case libraries as well as assistance for virtual tumour boards, collaborative research, and training programs. It ensures service continuity during crises such as pandemics or natural catastrophes by allowing for remote diagnostics.^17,18
• ➢Automation- AI-integrated digital pathology for automated diagnosis, increases workflow efficiency, diagnostic accuracy, and enables faster clinical decision-making. Automation ensures uniformity and decreases human error, allowing pathologists to focus on complex cases while speeding up the diagnosis process. Beyond diagnostics, AI integration also includes automatic reporting. AI systems may produce detailed, standardised reports that include diagnostic results, pertinent metrics, and suggestions. These automated reports can be tailored to institutional norms, improving communication consistency and streamlining workflow in healthcare settings. This strategy also improves data management, reporting standardisation, and integration with larger healthcare systems, ultimately leading to better patient outcomes.¹⁹
• ➢Collaboration- Collaboration in digital pathology, particularly with AI integration, promotes dataset sharing, multi-center validation, and interdisciplinary research, all of which are critical for moving the field forward and improving therapeutic outcomes. Collaboration enables researchers and pathologists from many institutions to share their data, resulting in more robust and representative datasets. This is especially crucial for developing AI models, because the quality and diversity of training data have a direct impact on the algorithms' accuracy and generalizability.
• ➢Support for Clinical Trials and Research-enables large-scale studies to be conducted for patient betterment and AI models trained on large datasets can also support biomarker discovery, aiding in the development of targeted therapies and improving clinical outcomes.²⁰

3.4. Challenges

• ➢Ensuring Data Security and Privacy- To prevent unauthorised access, breaches, or misuse of this data, stringent cybersecurity measures must be adopted. Encryption is a critical safeguard that ensures all data are securely encoded and cannot be accessed by unauthorised persons. Access control procedures are critical for restricting access to patient data to authorised staff only, ensuring that only those with the relevant rights can see or edit the data. Additionally, multi-factor authentication (MFA) and role-based access controls can improve security by confirming user identities. To comply with privacy standards such as General Data Protection Regulation, institutions must ensure that their data storage, processing, and sharing methods meet these legal requirements. In addition, secure cloud storage solutions and data anonymisation techniques should be used to preserve patient confidentiality and prevent data from being identified when shared for study.²¹
• ➢Handling Large Volumes of Data- AI-based analysis necessitates modern infrastructure to provide consistent data management. While cloud solutions are convenient and scalable, they incur continuous expenditures, whereas local systems require specialised support. Strategic investment and infrastructure planning are critical to efficiently manage these enormous datasets and guaranteeing the successful adoption of AI in digital pathology.
• ➢Addressing Bias in AI Models- Algorithms' effectiveness relies on the quality of data, and biases in training datasets can leads to inaccurate diagnosis, especially in diverse populations. Bias can originate from a variety of sources, including imbalanced datasets, inaccurate annotations, institution-specific image capture methods, and subjective labelling in borderline circumstances. These biases can lead to systemic errors, thereby reducing diagnostic accuracy while also raising ethical and legal concerns. To address this, it is critical to ensure dataset diversity by integrating samples from various populations, institutions, and disease presentations along with cross-institutional data sharing and multi-center validation.¹⁵
• ➢Regulatory & Standardization Issues for establishing a network of AI for healthcare system in India- AI in histopathology is still evolving and it confronts major legal and standardisation issues due to the dynamic nature of AI technology and regulatory bodies are yet to establish clear standards for development, validation, and quality control. Regulatory organisations have failed to develop frameworks expressly for AI tools in pathology, creating ambiguity about approval processes, performance monitoring, and liability in the event of diagnostic errors. To enable safe and dependable AI deployment, the industry must set worldwide standards, create dynamic regulatory models to accommodate changing AI technology, and put in place clear ethical and legal frameworks.²² Thus, building an AI-powered healthcare network can revolutionize medical diagnostics and treatment.

3.5. Our experience and recommendations

• ➢Besides following protocols and comprehending a systematic analysis, few of the major challenges faced by our own experience in AI-driven diagnostics is to develop ML algorithms which requires training the system by annotating the images across innumerable identifying features. Histopathological evaluation is a repetitive and labour-intensive practice that can be impacted by the pathologist's experience. Furthermore, increased workload and varied pathologist's skill have an impact on the consistency and reliability of histopathological image analysis. This subjectivity may result in diagnostic variability, misunderstanding, and limited diagnostic accuracy. As a result, we strongly propose that observers be trained and calibrated to increase annotation uniformity, hence increasing interobserver reliability and accuracy. This would help shorten the lag time between observer annotations and gradually induce observer agreements on disagreements.²⁵ To address this difficult task, we advocate working in tiers, beginning with basic characteristics and progressing to sophisticated features, and annotating photos concurrently by many observers to reduce interobserver variability and increase speed.²⁴
• ➢Additionally, the lack of ground truth consensus and annotation protocols and limited access to high-resolution digital tools across institutions further complicates consistency in data labelling. In many instances, there is no single “correct” label for complex histopathological patterns. Different pathologists may annotate the same feature differently, especially in dysplasia or borderline malignancy cases, leading to inconsistencies in training data for AI models.²³
• ➢To overcome these problems, employing standardised annotation processes and consensus-based approaches, such as expert review sessions, can assure consistent labelling of complicated cases and reduce variability between institutions. Ground truth agreement can be obtained by expert annotations and cross-institutional collaborations, resulting in reliable datasets for AI model training. Continuous training and calibration sessions, as well as interdisciplinary collaboration and the development of open-source databases, might help to hasten the process while assuring diverse expert input, so strengthening the resilience of AI models. As a result, standardised language and ontologies are crucial for machine learning-based image analysis, as conflicting labels lower model accuracy.²⁴
• ➢Despite advances in digital pathology, pathologists continue to face various obstacles when analysing and annotating image data. Manual annotation is time-consuming, laborious, and subject to inter- and intra-observer variability, particularly in borderline or ambiguous circumstances. The intricacy of tissue architecture, overlapping cellular characteristics, and differences in staining quality can make it challenging to delineate regions of interest accurately²⁶ (Fig. 2).
• ➢With increasing image volumes in clinical and research settings, pathologists often face difficulties in maintaining precision while managing workload as prolonged annotation sessions can lead to cognitive fatigue, decreasing accuracy and increasing error rates, which may affect the quality of training data needed for AI model development. These challenges underscore the need for advanced tools that can support and streamline the annotation process without compromising diagnostic integrity.²⁷

Fig. 2.

Challenges faced in Annotating Histopathology image.

Using a LAN connection for collaborative histopathology image annotation allows numerous pathologists to work together, minimising workload and cognitive strain. It enables real-time access to high-resolution images, faster data processing, and synchronised updates, resulting in increased workflow efficiency. AI solutions can provide real-time feedback on annotation accuracy, ensuring high-quality data and facilitating collaborative decision making. The centralised storage of images and annotations on a server assures data consistency, version control, and secure access, while real-time communication capabilities promote collaboration.^23,26 This arrangement improves productivity, decreases annotation time, and enables a more accurate and streamlined AI model training procedure, making it suitable for clinical and research settings (Fig. 3).

• ➢We also recommend automated pre-annotation AI technologies to assist in detecting possible ROI based on learnt patterns, hence decreasing the manual labour required for annotation. Using hybrid techniques and AI-pathologist collaboration to expand capabilities allows pathologists to examine and fine-tune these suggestions, combining human knowledge in diagnosis with AI-driven analysis to improve accuracy, particularly in complicated cases. Thus, improved digital tools, collaborative frameworks, and standardized guidelines are required to ensure that image analysis and annotation processes are both efficient and clinically robust, ultimately supporting the development of AI systems that are accurate, generalizable, and ethical.

Fig. 3.

Recommended workflow for Digital Image Analysis.

4. Conclusion

AI-assisted annotation tools to automatically segment tissues, identify histological features, and even suggest labels, allowing pathologists to review and validate rather than the routinely employed manual process can improve the efficacy (Fig. 4). This approach optimizes the process, maintaining expert oversight while drastically reducing the manual burden. By employing these strategies, it is possible to improve the efficiency, accuracy, and consistency of image analysis and annotation in digital pathology, ultimately enhancing diagnostic outcomes and supporting the integration of AI into clinical workflows.

Fig. 4.

Uses of AI in Histopathological image analysis.

Patient's/guardian's consent

Not applicable.

Ethics approval and consent to participate

Not applicable.

Sources of funding

NIL.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.