Agxio® in Action: Explainable AI in Oncology: Unlocking Trust and Precision in Cancer Care through our THEIA platform
Agxio recently worked with a private dermatology clinic and technology provider to a series of hospital trusts to develop a ground-breaking AI prediction engine for the top 20 skin cancer conditions which account for c95+% of all cases. The results based on our Apollo engine were remarkable but the challenges were significant. One of the key obstacles we experienced was the need to manage the expectations of the non-technical AI audiences on how to deploy AI. Many non-experts expect AI to be black boxes that produce ‘magical outcomes’ however as a leader in this field, Agxio has set since day one the requirement to ensure that all of our models are explainable and pass the human intuition test.
Furthermore, this project highlighted the lack of understanding that rules-based systems are an important part of AI development alongside more advanced neural network methods. Indeed, medical regulation often requires rules-based, known as determinative AI systems, as their core. The other options for model classes are discriminative and generative AI solutions which require more advanced explainability frameworks.
Artificial Intelligence (AI) is revolutionizing oncology, from cancer detection and diagnosis to treatment planning and prognosis. However, the complexity of AI models, particularly deep learning systems, often makes them “black boxes,” providing results without clear reasoning. In a field as critical as oncology, where decisions have life-changing consequences, trust, transparency, and interpretability are non-negotiable. Enter Explainable Artificial Intelligence (XAI)—a framework that ensures AI models are not only powerful but also understandable and accountable.
This article explores the key elements of XAI in oncology, why they matter, and how they are reshaping the future of cancer care.
Why is Explainable AI Crucial in Oncology?
In oncology, AI systems are used to analyse medical images, predict cancer risk, classify tumour types, and guide treatment decisions. However, clinicians and patients need to trust these systems before incorporating them into critical workflows. XAI ensures that AI models provide interpretable and transparent outputs, addressing key concerns such as:
- Trust: Clinicians must understand how an AI model arrived at its recommendation.
- Accountability: Patients and regulatory bodies need assurance that AI systems meet the highest safety standards.
- Collaboration: XAI enables clinicians and AI systems to work together effectively, improving outcomes.
Without XAI, even the most accurate AI systems risk rejection due to lack of trust and transparency.
Key Elements of XAI in Oncology
- Interpretability
Interpretability allows clinicians to understand the reasoning behind AI predictions.
- Localized Explanations: Tools like heatmaps or saliency maps highlight specific regions in medical images (e.g., CT scans, MRIs, or pathology slides) that influenced the model’s decision. For example:
- In breast cancer detection, a heatmap may highlight microcalcifications on a mammogram.
- Feature Importance: Models rank the importance of various features (e.g., tumor size, texture, or shape) to explain their predictions. This aligns with the clinical reasoning process.
- Human-Readable Outputs: AI explanations must be presented in ways that oncologists and pathologists can easily interpret, enabling informed decisions.
- Transparency
Transparency ensures that AI systems operate with clear mechanisms and reasoning.
- Model Architecture Transparency: The structure and functioning of AI models should be documented, enabling clinicians to understand how data is processed.
- Algorithmic Documentation: Detailed documentation of training data, preprocessing, and decision-making pathways builds confidence in the AI system.
- Auditability: Logging the AI’s decision-making process allows for post-analysis, enabling users to verify or challenge predictions.
- Reliability
Reliability ensures that AI systems consistently deliver accurate results across diverse scenarios.
- Robustness to Variability: XAI models must handle differences in imaging equipment, patient demographics, and clinical settings without performance degradation.
- Example: An AI model for melanoma detection should work equally well on patients with light or dark skin tones.
- Error Detection: XAI systems should flag low-confidence predictions, prompting human review to prevent misdiagnoses.
- Trust and Accountability
Trust is foundational for integrating AI into clinical oncology.
- Explainable Decision Pathways: XAI clarifies both the “how” and “why” of a prediction, ensuring clinicians can trust the AI’s outputs.
- Example: Explaining that a lung cancer diagnosis was based on irregular nodule borders and high nodule density.
- Human Oversight: XAI allows clinicians to override or refine AI decisions when necessary, maintaining human accountability.
- Bias Mitigation: XAI must identify and address biases in training data to ensure fair and equitable performance across all patient populations.
- Patient-Centric Explanations
XAI facilitates communication with patients, empowering them to understand and participate in their care.
- Simplified Outputs: AI results should be translated into patient-friendly language. For instance:
- Explaining that the size and shape of a tumor influenced a recommendation for surgery.
- Transparency in Risk Predictions: Patients deserve clear explanations for prognostic predictions, such as survival rates or recurrence risks, fostering trust and shared decision-making.
- Multimodal Data Integration
Oncology involves diverse data types, including imaging, genomics, and clinical records. XAI must handle and explain insights from multiple sources.
- Cross-Modal Explanations: XAI clarifies how different data types contribute to its predictions.
- Example: Explaining that a lung cancer prediction incorporates nodule density (imaging), genetic mutations (genomics), and smoking history (clinical records).
- Data Interactions: Highlighting how specific data points reinforce or modify the overall prediction.
Applications of XAI in Oncology
- Cancer Detection and Diagnosis:
- Radiology: XAI systems identify and explain suspicious lesions in mammograms, CT scans, and MRIs.
- Pathology: Heatmaps highlight cancerous regions in histopathological slides, guiding pathologists.
- Tumor Classification and Grading:
- AI systems classify cancer subtypes (e.g., adenocarcinoma vs. squamous cell carcinoma) and explain key features influencing the classification.
- Treatment Recommendations:
- XAI models recommend personalized therapies by analyzing tumor characteristics, patient history, and genomic data.
- Prognosis and Risk Prediction:
- AI predicts recurrence risks or survival rates, explaining which factors (e.g., tumor size or genetic markers) contributed to the prediction.
- Workflow Optimization:
- Pre-screening tools prioritize high-risk cases, while quality control systems flag artifacts or inconsistencies in medical images.
Challenges in Implementing XAI in Oncology
- Data Limitations:
- XAI models require large, diverse datasets for training. Biases or gaps in these datasets can compromise model performance.
- Complexity vs. Simplicity:
- Balancing detailed explanations with usability is a challenge, as overly complex outputs may confuse clinicians or patients.
- Regulatory Compliance:
- Meeting regulatory standards for clinical AI systems, such as FDA approval, involves extensive validation and documentation.
Future Directions for XAI in Oncology
- Explainable Multimodal AI:
- Integrating imaging, pathology, genomics, and clinical data into a unified system that explains how each modality contributes to its predictions.
- Federated Learning:
- Training models across institutions without sharing sensitive data ensures diverse and robust AI systems while preserving privacy.
- Real-Time Decision Support:
- Deploying XAI in real-time diagnostic tools, such as AI-powered surgical guidance systems, to support clinicians during procedures.
- Continuous Learning:
- XAI systems that learn from clinician feedback to improve over time, adapting to new data and evolving oncology practices.
Conclusion
Explainable AI (XAI) is essential for unlocking the full potential of AI in oncology. By providing transparency, interpretability, and reliability, XAI ensures that clinicians and patients can trust AI-driven tools in critical cancer care workflows. As AI technologies evolve, integrating XAI into oncology will not only enhance diagnostic precision and treatment personalization but also ensure ethical and patient-centered care.
The future of oncology lies in the seamless collaboration between humans and machines—XAI is the bridge that makes this partnership possible.
Contact info@agxio.com to explore this in more detail or to have a demonstration of our THEIA platform in action.