Interpretable artificial intelligence in radiology and radiation oncology

Area of Science:

• Medical Imaging and Artificial Intelligence
• Machine Learning Interpretability
• Clinical Decision Support Systems

Background:

• Artificial intelligence (AI) is increasingly integrated into clinical practice, particularly in radiology and radiation oncology.
• Applications span image segmentation, diagnosis, treatment planning, and prognosis prediction.
• Ensuring the accuracy and trustworthiness of AI models requires understanding their internal logic.

Purpose of the Study:

• To review core concepts of interpretability in machine learning for healthcare.
• To present state-of-the-art methods for understanding AI models in medicine.
• To discuss challenges, limitations, and provide examples of medical applications.

Main Methods:

• Literature review of interpretability techniques for machine learning models.
• Analysis of methods for evaluating model understanding.
• Identification of challenges and limitations in AI interpretability.

Main Results:

• Discussion of key interpretability concepts relevant to clinical AI.
• Overview of current methods for explaining AI model behavior.
• Exploration of evaluation metrics and practical considerations.

Conclusions:

• Interpretability is vital for the adoption and trust of AI in clinical settings.
• Understanding AI decision-making enhances diagnostic accuracy and treatment planning.
• Addressing challenges in AI interpretability will facilitate its broader medical application.