HistoPathExplorer: Empowering Clinicians to Translate AI Advances in Histopathology into Oncology Practice

The Histopathology Dashboard Helping Clinicians Translate AI Research into Oncology Practice

Artificial intelligence (AI) is transforming histopathology by enabling faster and more consistent analysis of digitised tissue slides. This technology supports cancer diagnosis by detecting microscopic patterns in tumour images, quantifying features, and assisting diagnostic decisions. AI tools are increasingly used for tumour classification, biomarker detection, and grading, offering scalable support to pathologists and oncologists in clinical workflows.

However, the rapid growth of AI research in oncology presents challenges. Clinicians often struggle to keep up with the volume of new studies, varying methodologies, and complex terminology. Comparing AI tools across different studies is difficult due to inconsistent reporting standards and diverse evaluation metrics.

Challenges with Datasets

A major issue lies in the datasets used to train AI models. Many are not representative of the full diversity of patient populations, which limits how broadly the models can be applied. Bias can arise when datasets focus heavily on common cancer types. For example, nearly half of AI studies concentrate on breast, lung, or colorectal cancers, leaving rare cancers less explored.

Performance metrics reported in studies also vary widely. Some report only a single measure like the area under the Receiver Operating Characteristic curve (AUC), while others include specificity and sensitivity. Differences in dataset quality and validation approaches make it harder to assess how well models will work in real clinical settings.

The “black box” nature of many AI systems, where decision processes lack transparency, adds to clinical hesitation. Additionally, limited long-term clinical data and treatment response information restrict AI development beyond diagnostic tasks. Most studies focus on detection and subtyping, with fewer addressing prognosis or treatment planning—areas critical for patient outcomes.

Establishing a Quality Index for AI Studies

To help assess the clinical readiness of AI tools, a quality index was developed. This index evaluates studies based on five key features:

• Reporting at least three performance metrics for comprehensive evaluation
• Benchmarking against existing models
• Providing access to code and data for reproducibility
• Using external validation to confirm generalisability
• Clearly describing methodology, pre-processing steps, and model architecture

This framework assists clinicians and engineers in quickly determining which AI models are reliable and applicable for clinical use, promoting informed decision-making.

Introducing HistoPathExplorer

To address the challenges of keeping pace with AI research, the HistoPathExplorer dashboard was created. It curates data from over 1,500 AI studies in digital pathology, offering a platform to:

• Identify, evaluate, and compare state-of-the-art AI methods across pathological applications
• Explore factors influencing AI performance, such as dataset quality and model design
• Understand challenges related to clinical translation, including generalisability and regulatory concerns
• Support decision-makers in synthesising evidence for clinical policy and guidelines

By providing detailed insight into AI studies, this tool empowers healthcare professionals and researchers to make better-informed decisions about adopting AI in cancer diagnostics.

Bridging AI Research and Clinical Practice

HistoPathExplorer makes complex AI models more accessible and interpretable for oncologists and pathologists. It highlights datasets from diverse populations to ensure AI tools are evaluated fairly. The dashboard’s transparent benchmarking and quality metrics accelerate evidence synthesis, helping clinical teams assess which tools are reliable and applicable.

The platform also points out underexplored areas, guiding research toward cancers and clinical needs that lack AI solutions. By fostering cross-disciplinary collaboration, it encourages the development of AI tools that integrate smoothly into diagnostic workflows and respect the critical role of human expertise.

Moreover, by showcasing publicly available datasets from different countries, HistoPathExplorer promotes global collaboration. This helps improve model generalisability and supports shared learning between engineers and clinicians.

Next Steps for HistoPathExplorer

The future development of HistoPathExplorer aims to allow users to explore, compare, and apply AI models on publicly available histopathology data directly within the platform. This will enable clinicians and researchers to visualize model outputs, interpret spatial tissue features, and connect findings with clinical variables — all without requiring programming skills.

This advancement could significantly support the practical deployment of AI in histopathology and contribute to better diagnostic accuracy and patient care.

For healthcare professionals interested in expanding their knowledge of AI applications in medicine, relevant courses and certifications are available through platforms like Complete AI Training.